You’ve spent hours crafting the perfect outreach message to a promising senior developer. They respond with interest, asking to connect in two weeks after their current project wraps up. You make a mental note to follow up… but two weeks later, that conversation is buried under 50 new messages, and the opportunity slips away.

Sound familiar?

As a technical recruiter at a fast-growing startup, I faced this reality daily. LinkedIn was our primary candidate source, but managing conversations felt like trying to organize a library with sticky notes. I needed an Applicant Tracking System (ATS), but our budget couldn’t stretch for both LinkedIn Recruiter and a premium ATS solution.

The LinkedIn Recruiting Paradox

LinkedIn is both essential and frustrating for modern recruiters:

• It’s where the talent lives (over 930 million users worldwide)
• The native messaging experience is chaotic, with important conversations quickly buried
• Premium tools like LinkedIn Recruiter are “very costly for a small company to bear” (as many recruiters lament)
• Messages sometimes appear in wrong threads, creating confusion (a particularly troubling bug)

Yet despite these frustrations, LinkedIn remains unavoidable. The question became: how could I transform this necessary but flawed platform into something that worked as efficiently as a purpose-built ATS?

What’s an ATS and Why Should You Care?

Before diving into solutions, let’s clarify what makes an Applicant Tracking System so valuable. An ATS is a recruiting software that centralizes candidate information and streamlines the hiring workflow.

According to LinkedIn Business, 86.1% of talent professionals say their ATS has increased hiring speed, while 78.3% report improved quality of hire. Nearly 99% of Fortune 500 companies rely on an ATS for good reason.

The core functions include:

• Organizing candidates into pipelines
• Tracking communication history
• Setting reminders for follow-ups
• Templating common messages
• Creating a central database of candidate information

I needed these capabilities within LinkedIn itself — and that’s where Kondo changed everything.

Building Your LinkedIn ATS: A Step-by-Step Guide

Here’s how I transformed LinkedIn into a fully-functional recruiter ATS using Kondo:

Step 1: Create a Candidate Pipeline with Labels & Split Inboxes

The Problem: The standard LinkedIn inbox is a chronological mess. There’s no way to organize candidates by role, stage, or priority.

The Solution: I created a structured pipeline using Kondo’s Labels feature.

I set up custom labels that mimicked our hiring funnel:

• [iOS Dev] - Sourced
• [iOS Dev] - Replied
• [iOS Dev] - Screening Call
• [iOS Dev] - Technical Interview
• [Android Dev] - Sourced (and so on for each role)
• Future - Keep Warm

Now when a promising candidate appears, I simply hit ‘L’, select the appropriate label, and they’re instantly categorized. Instead of a single crowded inbox, I can click on any label to see a dedicated inbox showing only those candidates.

This transforms my workflow from “scanning a firehose of mixed messages” to “focusing on specific candidates at specific stages” — exactly like in Greenhouse or Ashby, but directly within LinkedIn.

Step 2: Never Drop a Candidate with Reminders (Snooze)

The Problem: A candidate asks to reconnect in two weeks, but their message quickly gets buried and forgotten.

The Solution: Kondo’s Reminders feature creates a reliable follow-up system.

When a candidate asks to follow up later:

1. I press ‘H’ (for “hold”) on their conversation
2. Select when I want to follow up (tomorrow, next week, or a custom date)
3. The conversation disappears from my inbox
4. When the date arrives, it reappears at the top of my inbox

This simple feature has dramatically improved my candidate experience. I never miss a follow-up, and candidates notice the consistency. One recently told me, “You’re the only recruiter who actually followed up exactly when you said you would.”

Step 3: Fly Through Outreach with Snippets & Keyboard Shortcuts

The Problem: Writing similar messages repeatedly is time-consuming, and copy-pasting often leads to embarrassing personalization errors.

The Solution: I created a library of message templates (snippets) for every stage of recruitment.

For example, I have snippets for:

• Initial outreach to passive candidates
• Scheduling screening calls
• Sharing job descriptions
• Requesting portfolio links
• Rejections (with varying levels of detail)

To use them, I simply type ‘;’ and select the appropriate snippet. Variables like {firstName} are automatically filled in, creating personalized messages in seconds.

Combined with keyboard shortcuts (J/K to navigate, E to archive, L for labels, H for snooze), I can process my inbox 3–4x faster than before. This means more time for meaningful candidate conversations and less time on administrative tasks.

Step 4: Tying It All Together with an Inbox Zero Workflow

The Problem: A cluttered inbox creates anxiety and ensures important messages get missed.

The Solution: I adopted the Inbox Zero philosophy, treating my inbox as a to-do list, not a storage unit.

For every message, I now make one of four decisions:

1. Reply immediately (if it takes <2 minutes)
2. Snooze (H) for future follow-up
3. Label (L) to categorize in my pipeline
4. Archive (E) to clear it from my inbox

This approach ensures every candidate message gets proper attention and nothing falls through the cracks.

A Real-World Example: Sourcing Senior iOS Developers

Let me walk you through how this system works in practice:

1. I use Boolean search on LinkedIn to find senior iOS developers matching our criteria
2. I send personalized outreach using my snippets (‘;initial-ios’)
3. As replies come in:

• Interested candidates get labeled [iOS Dev] - Replied and I send a follow-up with more details
• Those wanting to chat get labeled [iOS Dev] - Scheduling and I send my Calendly link
• Those not interested but promising for future roles get labeled Future - Keep Warm and archived
• Those needing follow-up later get snoozed with ‘H’

4. After screening calls, candidates move to [iOS Dev] - Technical Interview or receive a rejection message

5. Throughout the process, all key information syncs to our Google Sheet tracking system via the crm sync

This workflow handled our most recent iOS developer search (35+ candidates in various stages) entirely within LinkedIn, without a single candidate falling through the cracks.

The Results: A Recruiting Command Center

Transforming LinkedIn into an ATS with Kondo delivered immediate benefits:

Quantifiable improvements:

• 50% reduction in time spent managing LinkedIn messages
• Zero missed follow-ups in six months
• 30% increase in positive candidate responses
• Significantly higher candidate satisfaction ratings

More importantly, the qualitative improvements were substantial:

• Reduced anxiety: No more worrying about missing important messages
• Better candidate experience: Consistent, timely communication at every stage
• Improved team visibility: Hiring managers can see candidate pipeline status
• Budget efficiency: Avoided the cost of both LinkedIn Recruiter and a premium ATS

Cost-Benefit Analysis: Is It Worth It?

Let’s look at the numbers:

• LinkedIn Recruiter: $8,999/year
• Enterprise ATS (like Greenhouse): $6,000-$10,000/year
• Kondo: $288/year

For startups and small companies where “LinkedIn recruiter or LinkedIn RPS is very costly to bear” (as noted in this discussion), the financial argument is clear. Even for larger companies, the productivity gains and improved candidate experience make a compelling case.

Beyond Recruiting: Other Applications

While I’ve focused on recruiting, this system works equally well for:

• Sales professionals managing prospect pipelines
• Account managers tracking client communications
• Networkers maintaining professional relationships

Anyone who uses LinkedIn messaging extensively can benefit from transforming it into a structured, efficient system.

Getting Started: Your First Steps

Ready to transform your own LinkedIn experience? Here’s how to start:

1. Analyze your workflow: What are your biggest LinkedIn pain points? Message organization? Follow-ups? Template messages?
2. Design your label system: Create labels that match your specific workflow (roles, stages, priorities)
3. Build your snippet library: Start with 3–5 of your most common messages
4. Adopt the Inbox Zero mindset: Process messages with the “reply, snooze, label, or archive” framework
5. Consider tools like Kondo that can enable this workflow

The goal isn’t to replicate every feature of an enterprise ATS, but to create a system that addresses your specific pain points and transforms LinkedIn from a source of chaos into a recruiting command center.

Conclusion

The LinkedIn paradox isn’t going away — it remains simultaneously essential and frustrating for recruiters. But with the right approach and tools, you can transform its limitations into strengths.

By bringing ATS-like organization to LinkedIn, you can create a recruiting workflow that’s efficient, reliable, and candidate-friendly, all without breaking your budget.

For me, this transformation has been game-changing. My LinkedIn inbox is no longer a source of anxiety but a powerful recruiting tool that helps me connect the right people with the right opportunities — consistently and confidently.

You log into LinkedIn and there it is again — a flood of unread messages. Cold pitches, connection requests, and that important lead you meant to follow up with last week but completely lost track of. Your heart sinks as you realize you’ve probably missed several valuable opportunities buried in this digital quicksand.

Everyday I get tonnes of valuable LinkedIn messages, and it’s a nightmare to manage! — A Reddit User

Every day, your LinkedIn inbox transforms from a potential goldmine of connections into an overwhelming source of anxiety.

Sound familiar? You’re not alone.

The LinkedIn Inbox Nightmare

For professionals serious about networking and business development, LinkedIn has become an essential platform. But with this importance comes a critical problem: inbox chaos that can derail your productivity and damage your professional relationships.

The challenges are numerous and painfully familiar:

• Message Overload: You’re bombarded with dozens or even hundreds of messages weekly, from genuine connection attempts to automated sales pitches
• Organization Nightmare: LinkedIn’s native inbox offers minimal filtering options, making it nearly impossible to prioritize important messages
• Follow-up Failures: Without a system, critical follow-ups fall through the cracks, potentially costing you valuable relationships and opportunities
• Mental Fatigue: The constant pressure to respond to everyone promptly creates genuine anxiety and decision fatigue
• Time Drain: You find yourself spending hours each week just trying to stay afloat in your message sea, time that could be spent on higher-value activities

This chaotic messaging environment has many wondering if real networking on LinkedIn is even possible anymore. “Are people actually forming real, valuable connections on LinkedIn anymore? Or is it just a numbers game now?” asked another discouraged user.

The frustration is real. You want to be responsive and build meaningful relationships, but the platform’s limitations make that increasingly difficult. You’ve tried setting aside dedicated “LinkedIn time” each day, but it’s not enough — the problem isn’t just about time management, it’s about infrastructure.

What you need isn’t more discipline or another productivity hack — you need a complete system redesign for how you handle LinkedIn communications.

Enter Kondo: The LinkedIn Inbox Solution You’ve Been Waiting For

This is where Kondo enters the picture — a purpose-built tool that transforms your chaotic LinkedIn inbox into a productivity powerhouse.

Described by users as “Superhuman for LinkedIn,” Kondo brings the same level of speed and efficiency to LinkedIn messaging that Superhuman brought to email.

It’s not just another external tool — it’s an entirely new interface for LinkedIn messaging that sits on top of the platform you already use.

“Try Kondo — Been using for a few months. Best Investment ever!” recommends one enthusiastic user in a Reddit thread about LinkedIn management tools.

How Kondo Transforms Your LinkedIn Experience

Kondo doesn’t just organize your messages — it fundamentally changes how you interact with LinkedIn networking. Here’s how:

1. Tame the Chaos with Labels & Split Inboxes

Kondo allows you to create custom labels for conversations (like “Hot Lead,” “Candidate,” or “Client”) and view them in separate, prioritized inboxes. This means you can instantly:

• Separate high-priority leads from general networking conversations
• Categorize conversations by project, role, or status
• Focus on exactly the conversations that matter most right now

Instead of scrolling endlessly through a single inbox, you can switch between focused views that contain only the conversations relevant to your current task.

2. Never Miss a Follow-Up with Reminders

One of the most powerful features of Kondo is the ability to snooze conversations and have them reappear at exactly the right time.

Simply hit the “H” shortcut while viewing any message, set a reminder time (tomorrow, next week, or any custom date), and the conversation disappears from your inbox until it’s time to follow up. When the reminder time arrives, the conversation pops back to the top of your inbox, ensuring you never miss an important follow-up again. This feature alone has saved countless sales opportunities and recruitment conversations from falling through the cracks.

3. Process Messages at Lightning Speed with Keyboard Shortcuts

Power users can navigate their inbox entirely with keyboard shortcuts, dramatically speeding up message processing:

• J/K to navigate up and down your message list
• E to archive a message
• H to set a reminder
• L to apply a label
• I to open the sender’s profile
• ; to insert a saved snippet

This keyboard-driven workflow allows you to process messages three times faster than the standard LinkedIn interface, turning inbox management from a chore into a breeze.

4. Save Time with Snippets

Stop typing the same messages over and over. Kondo’s snippets feature lets you save frequently used message templates and insert them with a simple shortcut.

Need to personalize? No problem.

Kondo snippets support variables like {firstName} that automatically populate with the recipient’s information, giving your messages a personal touch without the extra typing.

5. Connect LinkedIn to Your Other Tools (Business Tier)

For teams that need to log LinkedIn activity in external systems like HubSpot, Salesforce, or Notion, Kondo’s Business tier offers powerful integration capabilities through webhooks or native integrations. This means LinkedIn conversation data can automatically sync to your CRM or other tools, eliminating manual data entry and providing crucial visibility across your team.

Is Kondo Right for You?

Kondo delivers the most value if:

• You receive 20+ LinkedIn messages daily
• You use LinkedIn as a key channel for sales, recruiting, or networking
• You’ve experienced the frustration of missing important messages
• You value efficiency and organization in your workflow

While there are other LinkedIn tools on the market, Kondo stands apart with its focus on inbox management rather than automation or outreach.

It’s designed for professionals who want to manage real conversations more effectively, not generate more automated messages.

The Verdict: LinkedIn Inbox Superhuman Status Achieved

LinkedIn has become an essential business platform, but its native messaging experience simply hasn’t kept pace with the needs of power users.

Kondo fills this gap brilliantly, transforming a frustrating experience into a productivity superpower. As one user put it: “”

For professionals who rely on LinkedIn messaging for business development, recruitment, or networking, Kondo isn’t just another tool — it’s the solution to a problem that costs real time, opportunities, and peace of mind.

Ready to transform your LinkedIn inbox from chaos to super-productivity? Try Kondo today and experience the difference for yourself.

You’ve created a brilliant piece of content, optimized it for search engines, and shared it across your social platforms. But in the era of AI-powered search and content generation, there’s a new frontier to conquer: getting your brand and content mentioned by AI tools like ChatGPT, Perplexity, and Gemini.

This isn’t just about vanity — it’s about staying relevant in a rapidly evolving digital landscape where more users are turning to AI assistants for information, recommendations, and answers. When your brand appears in AI-generated responses, you gain visibility, authority, and trust in ways traditional marketing can’t match.

But how exactly do these AI systems decide what to mention? Let’s pull back the curtain on how chatbots and Large Language Models (LLMs) generate content, and what you can do to increase your chances of being included in their responses.

How Chatbots & LLMs Generate Content?

To understand how to get mentioned by AI, you first need to understand how these systems actually work.

Large Language Models like those powering ChatGPT, Perplexity, and Gemini are trained on vast datasets of text from the internet. However, these foundations models (FMs) are updated infrequently — they’re not continuously learning new information in real-time.

For example, GPT-4 has a knowledge cutoff date, after which it doesn’t “know” about new events, publications, or data. This presents an obvious limitation: how can an AI assistant provide up-to-date information if its knowledge is frozen in time?

The answer lies in supplemental tools and functions that these services integrate with their base models.

The Three-Layer System of AI Content Generation

Modern AI assistants typically rely on a three-layer approach to generate responses:

1. Foundation Model Knowledge: The core knowledge baked into the model during its pre-training phase.
2. Tool Integration: Functions that allow the model to perform real-time web searches, access databases, or retrieve information from knowledge bases through techniques like Retrieval-Augmented Generation (RAG).
3. Response Synthesis: The final step where the AI stitches together information from its foundation knowledge and newly retrieved data to create a coherent, helpful response.

This hybrid approach means that your content can appear in AI responses through multiple pathways — some immediate and others taking much longer to materialize.

3 Ways Your Content Gets Into AI Responses

There are three primary ways your content or brand mentions can find their way into AI-generated responses:

1. Foundation Model Training

The most fundamental but slowest path is through foundation model training. When companies like OpenAI, Google, or Anthropic train a completely new model version (like the jump from GPT-3 to GPT-4), they use massive datasets that may include your content.

Timeframe: Very slow (often years between major model releases)
Requirements: Your content must exist on the public internet during the model’s data collection phase
Visibility impact: Highest potential impact, as your content becomes part of the model’s core knowledge

2. Model Fine-tuning and Updates

AI companies occasionally release minor upgrades or specialized versions of their models without complete retraining. For example, GPT-4.1 vs GPT-4o, where the former is specialized for coding tasks. These updates may incorporate more recent data or focus on improving specific capabilities.

Timeframe: Moderate (months between updates)
Requirements: Your content needs to be recognized as high-quality and relevant to the specific capabilities being enhanced
Visibility impact: Moderate to high, depending on the focus of the update

3. Real-time Web Search Integration

The fastest and most direct route is through web search integration. When users ask questions that require up-to-date information, AI assistants like ChatGPT (with Browse), Perplexity, and Gemini will perform a search and incorporate the results into their responses, often with citations.

Timeframe: Immediate (as soon as your content is indexed by search engines)
Requirements: Your content must rank well in search results for relevant queries
Visibility impact: Most immediate but potentially temporary, as search results change over time

This third pathway — web search integration — is particularly important because it’s where Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) come into play.

What’s AEO & GEO?

Search Engine Optimization (SEO) has been the cornerstone of digital visibility for decades. Now, with the emergence of AI assistants as new interfaces for information retrieval, two new optimization approaches are gaining traction:

Answer Engine Optimization (AEO)

AEO focuses on optimizing content to be featured as direct answers to questions in search engines and AI assistants. It’s about structuring information to directly address user queries in a format that’s easy for AI systems to extract and present.

Generative Engine Optimization (GEO)

GEO takes this a step further, focusing specifically on optimizing content to be included in AI-generated responses from tools like ChatGPT, Perplexity, and Gemini. It considers not just how search engines index your content, but how LLMs process, understand, and incorporate it.

While these may sound like completely new disciplines, they’re actually extensions of good SEO practices, with some important nuances.

Should I shift my focus from SEO to AEO/GEO?

Despite what some might claim, SEO is not dead — it’s more important than ever. The key difference is that the fastest way to get your content into LLM responses (via AEO or GEO) is still by ranking high on the search results that these models access.

While there are reports that some AI companies (notably OpenAI with ChatGPT and Perplexity) are building their own search engines, they still largely rely on established search engines like Google and Bing behind the scenes.

Search engines still work by crawling and indexing the internet, then determining content authority using signals like:

• Backlinks from reputable sites
• Author and domain authority
• Content quality and relevance
• User engagement metrics
• Technical optimization factors

Given this reality, traditional SEO remains foundational to AI visibility. If your content doesn’t rank well on search engines, it’s unlikely to appear in AI-generated responses that rely on those search engines for real-time information.

How to Maximize Your Chances of AI Mentions?

To ensure your content is crawled, indexed, and ranked highly enough to be included in AI responses, you need to be “loud” enough in the digital ecosystem. This is where classic content marketing meets some new AI-age tactics. Here’s how to establish an omnipresent digital footprint:

1. Own a Company Blog with Expert Content

An on-domain blog is still your content hub. Publish high-quality, in-depth posts that answer key questions in your industry. Not only does this help with SEO (ranking on Google), it also provides fodder that an AI might quote.

Use clear headings and Q&A style sections (great for AEO) — for example, an FAQ on your blog about “How does XYZ work?” might be directly lifted by an answer engine. Regular blogging also means more pages for Google to index and thus more chances an AI finds you via search.

Focus on E-E-A-T: demonstrate your expertise and experience in each post (original research, insights, case studies). A well-respected blog post might even end up referenced in the training data of the next GPT model if it gains enough traction.

2. Run a Niche Newsletter or Substack

Newsletters can build a loyal audience and establish you as a thought leader. While the content of an email isn’t indexed by Google, many newsletter platforms (like Substack) also post your issues to a public URL. Those pages can rank or be accessed.

Moreover, if your newsletter analysis or data insights get talked about (or you get backlinks from others referencing it), that strengthens your content’s footprint. Some AI models have been known to ingest popular public Substack posts or at least the discussions around them. Even if not, a newsletter grows brand search volume (people searching your newsletter or brand name), which as noted can indirectly improve AI visibility. Use the newsletter to seed ideas and get them shared; the more your ideas are discussed on the open web, the more AI will ultimately consume them.

3. Publish on Platforms like Medium and LinkedIn

Don’t rely solely on your own site. Syndicate or contribute content to high-visibility platforms. Writing on Medium, for example, can tap into an existing audience and SEO juice — Medium posts often rank well on Google for long-tail queries, which means an AI doing a web search might encounter your Medium article.

Likewise, publishing thought pieces on LinkedIn or industry sites can get your name out there. These platforms typically have high domain authority, increasing the likelihood that your content is considered a “trusted source.” (Just be mindful of duplicate content issues — if you republish, consider using canonical links or wait a bit after posting on your blog first.)

4. Engage in Reddit Threads and Niche Communities

It might not feel “professional” to spend time on Reddit, but from an AI visibility standpoint, Reddit is gold. Reddit threads often rank on Google for all sorts of queries (e.g., “Best CRM for startups reddit”), and they contain candid, content-rich discussions.

By contributing genuinely useful answers or mentioning your product in context on Reddit, you’re not only doing community marketing — you’re creating content that could be scraped or retrieved by an AI. In fact, Reddit content is so valuable that OpenAI is directly incorporating it to train models. That means your explanations or insights on Reddit could literally become part of ChatGPT’s knowledge.

Just remember to follow community rules and avoid blatant self-promo (which can backfire). Other Q&A platforms like Stack Exchange, Quora, Hacker News have similar benefits: they get scraped/indexed and often surface in search results, so a helpful answer there can indirectly feed AI answers later.

5. Create Video Content (YouTube, TikTok) with Transcripts

Don’t ignore video just because AI seems text-centric. YouTube is one of the world’s biggest search engines itself, and Google often shows video results (with transcripts or key moments) for queries. AI models are rapidly becoming multimodal, meaning they can understand text, images, and audio. Google’s Gemini is expected to handle text and videos, and GPT-4 already has vision capabilities.

By creating videos (webinars, how-tos, thought leadership talks) and ensuring they have good descriptions or even uploading transcripts, you create another avenue for your content to be indexed and understood by AI. A chatbot in the future might directly pull an explanation from a YouTube transcript. TikTok is more of a wildcard for AI, but it drives trends — a concept popularized on TikTok might be discussed on the wider web (articles, tweets, etc.), which then become training data. Also, Google has started indexing some TikTok content. So short videos can amplify your message which then gets captured in text elsewhere.

6. Leverage Visual Platforms (Pinterest, Infographics, Slides)

Pinterest might seem like just a recipe and decor site, but don’t underestimate it for certain content. Pinterest images often rank in Google Image search. While an AI might not “see” your infographic image today, the alt-text or description of that image is crawlable. And with multimodal AI, tomorrow’s ChatGPT could analyze an image’s content too.

By creating visual assets (infographics, diagrams, charts) and sharing them on high-DA platforms (Pinterest, Imgur, Slideshare, etc.), you increase the chances of your ideas being ingested. Moreover, those visuals often get re-shared in blog posts or news (with credit to you), yielding backlinks and mentions. Think of visual content as another way to amplify and curate your most important points — which is exactly what you want for AI amplification (more on that when we discuss IDA).

7. Foster Discussions and Citations

Beyond creating your own content, get other reputable voices talking about you. Guest post on well-known blogs, appear on podcasts or webinars (which often get summarized online), speak at conferences (speeches sometimes get uploaded or live-tweeted). These activities build a trail of mentions on trusted sites.

For instance, if your SaaS is mentioned favorably in a TechCrunch article, that article will likely be in Google’s index and an AI searching “top SaaS in X category” might very well cite TechCrunch (and thereby your product). The more third-party authority signals you earn — press coverage, journal citations, influencer reviews — the more you “feed” the machine with evidence that your brand is relevant and authoritative.

This approach helps establish your content as the go-to resource in specific niches, increasing the likelihood that AI systems will reference it when relevant topics come up.

How New Content is Introduced into Foundation Models?

So far, we have discussed how chat assistants uses web search to supplement its limited training data. But the question remains on how can we ensure our content are trained into the foundation models themselves?

That’s where IDA comes in. One key methodology for AI advancement is Iterated Distillation and Amplification (IDA), famously used in training AlphaGo.

IDA consists of two main processes:

Amplification

Given a model M0, more resources are deployed to improve its performance. This might involve:

• Allowing the model to “think” longer
• Running many copies in parallel
• Providing the model with tools to perform real-time web searches, query propritary APIs, access databases, or retrieve information from knowledge bases
• Implementing intense evaluation processes
• Curating only the highest quality answers

This resource-intensive process creates a temporarily improved system, Amp(M0), that produces higher quality outputs but at the cost of speed and efficiency.

Distillation

The improved but resource-heavy Amp(M0) is then used to train a new, more efficient model M1 that aims to produce similar high-quality outputs but with less computational resources. The result is a smarter, more capable base model.

This cycle repeats, with each iteration potentially incorporating new data sources that help the model make better judgments about what information is valuable and authoritative.

IDA — Your Gateway into Foundation Models

Why does IDA matter when we talk about getting your content into AI responses, especially into foundation models themselves, rather than just through external tools like search integrations?

While tools and web searches enable immediate content inclusion in AI-generated responses, they don’t guarantee permanent visibility. Each time a user asks a question, the AI must search again, and your visibility is constantly at the mercy of real-time ranking shifts.

Iterated Distillation & Amplification could make models more likely to include content that has been “proven” useful or true through iterative curation.

Consider that future foundation models use an IDA-like process during training. This could mean that if there are certain reliable pieces of information (say, well-regarded articles, verified facts, highly cited explanations), those might get amplified through an oversight process (curators or algorithms highlighting them) and then distilled into the model. In other words, IDA could make models more likely to include content that has been “proven” useful or true through iterative curation.

Another angle: Amplification in today’s retrieval-augmented systems means an AI might use tools (like search) and see your content multiple times for various queries. If those systems also have a learning component, they might start to weigh frequently retrieved/cited content more heavily. Some speculate about a feedback loop: if ChatGPT (with browsing) keeps pulling data from certain sources to answer questions, OpenAI might notice and ensure the model “remembers” those sources better in future updates (a form of distillation of usage). This is somewhat speculative, but it aligns with the idea of models amplifying via retrieval and then distilling knowledge.

In other words, through IDA, your content doesn’t just appear temporarily — it becomes a permanent fixture within the AI’s core intelligence, ensuring sustained visibility and authority.

What’s The Future of SEO, AEO, and GEO?

As we look ahead to 2025 and beyond, several trends are reshaping how content gets discovered and featured in both traditional search and AI-generated responses:

1. Semantic Understanding Over Keyword Matching

Search engines and AI assistants are increasingly prioritizing semantic understanding over simple keyword matching. This means:

• Intent matters more than exact match: Understanding the why behind queries
• Natural language processing: Interpreting conversational queries more effectively
• Topic coverage: Comprehensive coverage of topics rather than keyword density

This shift requires content creators to think more about addressing user needs holistically rather than optimizing for specific keyword phrases.

Optimizing purely for exact keywords (“best CRM 2025” repeated 10 times) is old news. Now it’s about semantic SEO — covering topics comprehensively and naturally so that AI can tell your content is relevant even if the user’s query words are different.

Large language models excel at semantic understanding; they will rewrite queries in their head and look for content that answers the intent. An AI search engine will favor a page that directly addresses the question over one that just happens to have the keywords.

In practice, focus on answering questions clearly, providing context, and using natural language in your content.

2. Multi-Modal Content for Enhanced User Experience

Currently, most LLMs are primarily text-based in both input and output, but this is rapidly changing:

• Visual content integration: Images and videos becoming searchable and referenceable
• Audio content indexing: Podcasts and audio content being incorporated into search results
• Interactive elements: Content that allows for engagement rather than passive consumption

OpenAI’s latest models can analyze images, Google’s Gemini is expected to be multimodal from the start, and other players are integrating video and audio understanding. This means your non-text content needs optimization too.

Ensure images on your website have descriptive alt text (both for accessibility and for AI — an AI that “reads” your image will likely check the alt text or surrounding text for context). For product-based businesses, feeding images with proper captions or schema (like Schema.org image metadata) could help AI recognize your products. For video, include transcripts or closed captions — not only does this help YouTube SEO, but those transcripts are what an AI will use to “understand” your video. Even audio (podcasts, etc.) should have show notes or transcripts.

Essentially, make each format of content as self-contained in meaning as possible.

3. The Golden Age of Long-Form Content

Despite fears that AI might make in-depth content obsolete, the opposite is happening:

• Training data preference: LLMs are often trained on comprehensive, long-form content
• Authority signals: Detailed content often signals expertise and authority
• Citation preference: AI tools preferentially cite comprehensive resources

Now is actually the golden age for creating definitive, authoritative long-form content that can serve as a foundation for AI training and citations.

In fact, expect the lines to blur between content made for humans and for AI. We already see AI-written content populating the web.

If that content is low-quality, it won’t rank well (Google’s algorithms, backed by the Helpful Content system, will downrank content that seems made just for SEO). But if it is high-quality, it can perform.

In other words, AI-generated content can rank well as long as it’s genuinely useful — Google explicitly says it rewards quality regardless of how it’s produced. This opens the door for you to use AI in content creation (to scale up blogs, generate ideas, etc.), but with the crucial caveat: always add the human touch of expertise and originality.

4. Community and User-Generated Content

The recent $60 million deal between Reddit and Google highlights the increasing value of authentic user discussions:

• Real user experiences: Content that captures authentic human experiences
• Diverse perspectives: Multiple viewpoints on topics rather than single authoritative statements
• Question and answer formats: Content structured around actual user questions

As one Reddit user observed: “Do you think giving Reddit 5x more traffic in the last 6 months part of the deal?” This partnership underscores the value of community content in both search and AI responses.

5. Content for Humans vs. Content for LLMs (e.g., LLMs.txt)

A fascinating development is the idea of formatting content specifically for AI consumption. You’re likely familiar with robots.txt (which guides search engine crawlers) and perhaps sitemap.xml (which lists your site’s pages). Now there’s talk of LLMs.txt – a proposed standard where site owners provide a concise, AI-friendly summary or index of their content to help language models use it.

The idea is to strip out the fluff (navigation, ads, etc.) and give AI models a clean roadmap to your site’s information. Companies like Anthropic are already exploring this, and tools are emerging to generate llms.txt files.

In practical terms, we might soon be maintaining two layers of content: one richly formatted for human readers, and one distilled for AI agents to ingest easily. It’s early days, but keep an eye on this trend.

If standards like llms.txt gain traction, you’ll want to adopt them to make sure AI agents can parse your site effectively. This could mean providing metadata about your content’s structure, or even offering direct “AI summaries” of each page that an LLM can grab. The goal is to avoid scenarios where an AI misinterprets or ignores your content because it got lost in the boilerplate.

Preparing AI-readable content feeds (while still prioritizing the human reader on your actual pages) might become a best practice.

The Interconnected Future of Search and AI

As we look ahead, the lines between traditional search and AI-generated responses will continue to blur. Google’s Search Generative Experience (SGE) and Microsoft’s integration of OpenAI technology into Bing are early indicators of this convergence.

In this evolving landscape, the most successful content strategies will be those that optimize for both human readers and AI systems simultaneously, without sacrificing quality or authenticity.

Key Takeaways for Success

1. Quality trumps quantity: Create fewer, better pieces rather than high volume of mediocre content
2. Omnipresence matters: Distribute content across multiple platforms to increase visibility
3. Authority compounds: Building expertise takes time, but yields increasing returns
4. Technical excellence is non-negotiable: Site speed, mobile optimization, and structured data remain crucial
5. Adaptability is essential: Stay informed about changes in how AI systems process and reference content

As one SEO professional wisely noted: “I know I have a lot more to learn and I’m humbled and appreciative when anyone in the field gives me advice or resources to learn.” This learning mindset is perhaps the most important factor for long-term success in the rapidly evolving fields of SEO, AEO, and GEO.

Conclusion: Embracing the AI-Enhanced Content Landscape

Getting mentioned by AI tools like ChatGPT, Perplexity, and Gemini isn’t about gaming the system — it’s about creating genuinely valuable content that these systems naturally want to reference because it provides the best answers to user queries.

Understanding the mechanisms through which your content can appear in AI responses — whether through foundation model training, model fine-tuning, or real-time web search integration — allows you to create strategic content plans that maximize your visibility across all potential pathways.

While the terminology and specific techniques may evolve, the fundamental principles remain consistent: create high-quality, authoritative content that genuinely addresses user needs, and ensure it’s technically optimized and strategically distributed across the digital ecosystem.

By embracing these principles and staying adaptable as the landscape continues to evolve, you can position your brand to thrive in the AI-enhanced future of digital content — not just being mentioned by AI, but being recognized as an authoritative source worthy of citation and recommendation.

Remember: the goal isn’t just to appear in AI responses today, but to build the kind of enduring authority that ensures you remain a go-to source as these systems continue to advance in the years ahead.

Common Questions About AI Mentions and Optimization

Q: Is SEO still important in 2025?

A: Absolutely yes. SEO is not only still relevant but more important than ever. While the mechanics and priorities have evolved, the fundamental goal remains the same: making your content discoverable and recognized as authoritative.

Traditional SEO practices like technical optimization, quality backlinks, and mobile-friendliness remain crucial. As one SEO professional noted: “I didn’t prioritize mobile optimization on my site when I should have; it was a major oversight.”

The difference now is that you’re optimizing not just for human searchers but also for AI systems that may reference your content. Good SEO practices form the foundation upon which successful AEO and GEO strategies are built.

Q: Can any company/tool guarantee mentions in ChatGPT or other tools?

A: No, they cannot. Be extremely skeptical of any service claiming to guarantee mentions in AI tools. Since these systems rely on complex algorithms and constantly evolving models, no one can truly guarantee inclusion.

However, understanding how these systems work allows you to significantly increase your chances through strategic content creation and distribution. Focus on services that help improve your overall content quality and distribution rather than those making unrealistic promises about AI mentions.

Q: Can I get onto LLM by writing blog posts?

A: Yes, you can. Blog posts remain one of the most effective ways to get mentioned by AI systems, especially when they’re comprehensive, well-researched, and published on platforms with existing authority.

For example, one of our clients, Playtours, ranks #1 in ChatGPT responses for relevant queries, with the cited source being a blog post published on Medium.com. The key factors were:

1. Comprehensive coverage of the topic
2. Publication on a high-authority platform (Medium)
3. Clear structure that made the content easy for AI to parse and reference
4. Regular updates to keep the information current

Q: Is there really a need to differentiate between SEO & GEO?

A: Not necessarily for practical implementation. While the terminology helps us understand different optimization targets (traditional search vs. AI systems), the actual practices significantly overlap.

Good content that works well for search engines typically works well for AI systems too. The primary differences lie in:

• Citation format: Optimizing for how AI systems attribute sources
• Question-answer structure: Making answers more extractable for AI systems
• Comprehensive coverage: Ensuring content thoroughly addresses topics from multiple angles

For most businesses, implementing solid SEO practices while being mindful of how AI systems process content is sufficient without needing entirely separate strategies.

Q: How can I ‘hack’ the authority of my content?

A: While there’s no true “hack” for authority (it must be earned), there are strategic approaches to accelerate authority building:

1. Build domain and author authority: Consistently publish quality content under your name and on your domain
2. Leverage established platforms: Publish on sites like Medium, LinkedIn, or industry publications that already have strong authority
3. Secure press mentions: Get featured in news outlets and industry publications through PR efforts
4. Participate in community discussions: Contribute valuable insights on platforms like Reddit, Quora, and industry forums
5. Create guest content: Write guest posts for high-authority sites in your industry
6. Collaborate with established experts: Co-create content with recognized authorities in your field

As one Reddit user advised someone looking to build authority in the crafts coaching space: “Aim to get your audience to think the following: This person knows me, understands my situation, knows exactly how I feel.”

Q: Is Reddit important for ranking on Google and LLM?

A: Yes, increasingly so. Reddit has secured a $60 million deal with Google, and its content is frequently featured in search results and AI training datasets. There are several reasons why Reddit is particularly valuable:

1. Real user discussions: Authentic conversations signal relevance and interest
2. Question-answer format: Matches how many people use search and AI assistants
3. Topic expertise: Subreddits create concentrated expertise in specific niches
4. Diverse perspectives: Multiple viewpoints on topics provide comprehensive coverage
5. Freshness signals: Regular updates and new discussions on trending topics

Participating thoughtfully in relevant Reddit communities can significantly boost your visibility in both traditional search results and AI-generated responses.

Q: Should I just spam with low-quality content?

A: Absolutely not. Low-quality content is not only ineffective but actively harmful to your brand and visibility prospects. As AI systems and search engines become more sophisticated, they’re increasingly able to identify and penalize content that doesn’t provide genuine value.

One Reddit user expressed concern about the impact of poor-quality content: “I guess the real world is very ‘toxic’ so it makes sense that the AI’s should be just as ‘toxic’.” This highlights how low-quality content can negatively influence AI training.

Instead, focus on creating fewer but higher-quality pieces that genuinely address user needs and demonstrate expertise. Quality over quantity is more important than ever in the age of AI.

Q: Can I rank with AI-generated content?

A: Yes, you absolutely can. The quality of the content matters far more than how it was produced. We’ve successfully ranked both on Google and in LLM responses for ourselves and our clients using AI-generated content.

However, this comes with important caveats:

1. Research is critical: AI content needs to be based on thorough research and accurate information
2. Human oversight: Review and editing by subject matter experts is essential
3. Unique insights: Adding perspectives and examples not readily available elsewhere
4. Value-first approach: Focusing on solving real user problems rather than just generating words

One content creator shared: “I’m still new to leveraging AI for things like writing blog posts, social media captions, etc.” To which another responded: “One tip that’s worked for me is using AI to get a first draft down quickly, and then fine-tuning it to make sure it still has that personal touch.”

Our workflow involves an intensive research process and allows humans to augment the AI-generated draft with their unique insights, resulting in high-quality content that ranks well while maintaining authenticity.

Q: How often are foundation models like GPT-4 retrained?

A: Major foundation models typically undergo complete retraining every 1–2 years, though this varies by company and model. Smaller updates and fine-tuning may happen more frequently.

Q: Do AI assistants always cite their sources?

A: Not always. While tools like Perplexity typically provide citations, others like ChatGPT may incorporate information without explicit attribution, especially for knowledge embedded in their foundation models.

Q: How long does it take for new content to appear in AI responses?

A: Content that’s discoverable through web searches can appear in AI responses within days or weeks of being indexed by search engines. However, inclusion in the foundation model’s knowledge may take years, until the next major model update.

Q: Are certain content formats preferred by AI systems?

A: Text-based content with clear structure (headings, lists, tables) is currently most easily processed by AI systems. However, as multimodal capabilities advance, other formats like video and audio are becoming increasingly important.

Q: Will writing specifically for AI compromise my content’s appeal to human readers?

A: Not if done properly. The best practices for AI visibility — comprehensive coverage, clear structure, authoritative sources — also enhance readability and value for human audiences. There should be no need to choose between optimizing for humans or AI.

Q: How can I measure if my content is being used by AI tools?

A: Direct measurement is challenging, but you can:

• Monitor referral traffic from AI tools that provide clickable citations
• Track brand mentions in AI responses to common queries in your niche
• Use specialized tools that simulate AI queries and track when your content appears
• Monitor indirect signals like increased traffic to pages that address questions commonly asked to AI assistants

By staying informed about these evolving technologies and implementing the strategies outlined in this article, you’ll be well-positioned to ensure your content remains visible and influential in the age of AI-assisted information retrieval.

Additional Resources

For those looking to dive deeper into the topics covered in this article, here are some valuable resources:

• OpenAI’s Introduction to ChatGPT Search — Learn directly from OpenAI about how ChatGPT integrates web search functionality.
• AI Alignment: Iterated Distillation and Amplification — A detailed exploration of the IDA concept and its implications for AI development.
• SearchEngineLand: The Proposed LLMs.txt Standard — Information about the proposed standard for guiding how LLMs access website content.
• SparkToro’s Guide to Brand Appearances in AI Responses — Practical strategies for increasing your brand’s visibility in AI-generated content.
• ML6: How LLMs Access Real-Time Data from the Web — Technical insight into the mechanisms that allow LLMs to retrieve and incorporate real-time information.

Blog article originally published on Synscribe’s blog

You’ve heard the hype about GPT-4o, the highly anticipated new AI model from OpenAI that promises to be a game-changer. But how does it actually stack up against other leading language models like Claude 3 and Gemini Pro?

In this article we will use comprehensive benchmark data that rigorously evaluated GPT-4o’s performance against Claude and Gemini across a wide range of quantitative metrics and qualitative use cases.

You can finally go beyond the hype to make a data-driven assessment of GPT-4o’s strengths, weaknesses, and unique differentiators.

Quantitative Comparisons

To rigorously evaluate GPT-4o against Claude and Gemini, researchers conducted a series of standardized benchmarks across a wide range of tasks and metrics. The benchmarking process followed established best practices to ensure fair and consistent evaluation.

Benchmarking Overview

The benchmarks covered a diverse range of tasks including:

• Multi-Model Language Understanding (MMLU): Evaluates general language understanding across multiple tasks like coreference resolution, word sense disambiguation, and natural language inference. Higher scores are better.
• General-Purpose Question Answering (GPQA): Tests question answering abilities on open-domain queries. Higher scores are better.
• Mathematics (MATH): Assesses capabilities in solving math word problems. Higher scores are better.
• Code Generation (HumanEval): Evaluates the models’ ability to generate correct code from problem descriptions. Higher scores indicate better coding abilities.
• Multi-Choice Generative Summarization (MGSM): Measures summarization capabilities by testing generation of concise summaries from longer text. Higher scores mean better summarization performance.
• Complex Question Answering (DROP): Evaluates question answering abilities that require reasoning over long contexts. Models are scored on F1, with higher being better.

Key metrics evaluated were accuracy, F1 score, and task-specific metrics like perplexity and pass rates. In general, higher scores indicate better performance.

Benchmark Results

This single table provides an overview of how the models performed across the diverse set of benchmarking tasks.

GPT-4o demonstrated elite-level performance, achieving scores above 85 on multiple benchmarks like MMLU, HumanEval, and MGSM.

However, no single model dominated across every task. Claude and Gemini models showed strengths in certain areas as well.

Crowdsourced Evaluation

While the standardized benchmarks provide objective quantitative comparisons, it’s also valuable to look at crowdsourced evaluations that incorporate real-world human judgments. The LMSYS Chatbot Arena is one such platform that has collected over 1,000,000 human pairwise comparisons to rank language models.

The LMSYS leaderboard uses these crowdsourced human evaluations to rank models on an Elo-scale, providing a qualitative view into their relative capabilities. The ranking factors in overall performance as well as strengths in areas like coding and handling longer queries.

Overall Ranking

( Source, retrieved 17 Jun)

The overall LMSYS ranking has GPT-4o in the top spot, followed by the Gemini models from Google and GPT-4 variants from OpenAI. Claude-3-Opus from Anthropic also makes an appearance in the top tier.

Average Win Rate Against All Other Models

One key output from the LMSYS data is the average win rate for each model when compared against all other models in pairwise tests, assuming uniform sampling and no ties.

This chart visualizes those average win rates:

( Source, retrieved 17 Jun)

While GPT-4o topped the charts with a 65% average win rate, Claude-3-Opus and the Gemini models also showed strong performance based on these crowdsourced evaluations.

The LMSYS data provides a valuable real-world complement to the standardized benchmarks. With both quantitative and qualitative perspectives, we can develop a comprehensive understanding of the relative capabilities of GPT-4o versus other leading language models.

Qualitative Comparisons

In addition to the quantitative benchmarks, real-world users and experts provided valuable qualitative insights on how GPT-4o truly compares to models like Claude and ChatGPT across different use cases. We make qualitative notes from several Subreddit threads for this section.

Language and Tone

Multiple users noted that Claude has a more personable, friendly, empathetic and expressive communication style compared to the more robotic and neutral tone of ChatGPT.

“Claude is more personable, friendly, warm and empathetic. ChatGPT, by contrast, gets too robotic.” (Source)

“(Claude) is much more natural in word usage as well as not repeating itself with the same phrase and using original verbiage.” (Source)

However, some preferred ChatGPT’s more neutral, matter-of-fact style: “If you like a more human tone, give Llama 3 a shot. Gemini’s also not bad, but it has a lot of silly refusals as well. I prefer the more neutral, robotic, matter-of-fact tone of GPT most times.” (Source)

Writing and Content Creation

For tasks like writing, content creation, and language processing, many found Claude to be superior to ChatGPT in areas like response length, eloquence, and avoiding telltale signs of AI-generated text.

“It isn’t as limited in response length, so I can work much faster. It never back-talks me or gives me some variation of “I can’t handle that request.” It doesn’t use insanely flowery language or stupid word choices that are a dead giveaway of AI-generated content.” (Source)

“As a writer, I find Claude to be more eloquent…” (Source)

“Claude beats chatGPT for writing healthcare education materials, hands down.” (Source)

“Claude Opus is far superior than ChatGPT 4 for copywriting. GPT has helps as an idea generator, but its writing is stiff. With Claude, I get a good rough draft of what I need from a few prompts, then I spend my time editing rather than writing.” (Source)

Coding and Programming

A consistent trend was the strong preference for Claude, especially Claude 3 Opus, over ChatGPT and GPT-4 models for coding tasks. Users cited Claude’s longer context window, better code reasoning, and superior handling of complex prompts as key advantages.

“Claude has consistently outperformed GPT-4 in the area of coding — generating templates, troubleshooting, and seeking explanations.” (Source)

“I’ve been using Claude 3 primarily for coding, and it’s been transformative compared to ChatGPT/GPT-4 for that. It’s superior to ChatGPT/GPT-4 for one major reason — its context window.” (Source)

“Claude opus gives very nice and thoughtful code, much much better then 4o…Claude has been better at esoteric C++ in my experience.” (Source)

“Claude is the best at correctly following complex instructions in the prompt. It handles coding very well, even better if using at low temperature.” (Source)

However, some users found GPT-4o to be superior for certain coding tasks:

“GPT4o is super fast, but I find it only really excels at pointing your camera at things, and asking somewhat involved questions about its contents…Reasoning can be more nuanced if prompted well.” (Source)

“The new llmsys leaderboard rankings came out yesterday and GPT-4o took the first spot on the code category, so it seems to be doing something right for most people testing code on the arena.” (Source)

Analysis and Context

Another key strength of Claude highlighted by users was its superior ability to handle analysis over long contexts and documents compared to GPT models.

“Claude now has a 200k token context window. (GPT-4 has max 128K)…For pure writing and responses, also for parsing large amounts of information and quickly learning a natural desired output, Claude 2 is far superior.” (Source)

“Claude’s context window makes it much more valuable than GPT4 if you want to feed Claude an entire codebase or a large chunk of it.” (Source)

“Claude is significantly better at reasoning. WAY better. I think this is evident in stress tests involving causal probability, fictional writing with dozens of characters and their interactions with the environment, induction and deduction on real-life problems, and coding creative stuff.” (Source)

Limitations and Drawbacks

While praising Claude’s strengths, users also highlighted some key limitations and drawbacks compared to GPT models:

“INSANE refusal rate and false positives due to censorship. Seriously, this is embarrassing. GPT-4O, on the other hand, has no moral training, so it won’t understand the difference between solving a riddle or saving a kitten. But will comply with like 98% requests.” (Source)

“It can’t surf the internet or produce images.” (Source)

“Claude may be slightly more prone to sycophancy, though it’s pretty common with LLMs in general. This is easily addressed with some promting I’ve found.” (Source)

Some also noted the limitations of ChatGPT’s chat interface for certain tasks:

“If you need a language model for analysis and iteration, you should be using the API. Claude’s chat interface…” (Source)

“ChatGPT has limitations in response length, which slows down work…ChatGPT struggles with memory issues when coding, forgetting progress from prior prompts.” (Source)

Overall, the qualitative feedback reveals key strengths of Claude like superior language quality, coding abilities, and context handling — while also highlighting areas where GPT models may still have an edge, such as avoiding censorship issues. Many took a hybrid approach, leveraging each model’s strengths for different use cases.

Verdict

Based on the comprehensive quantitative benchmarks and real-world qualitative feedback, we can develop a nuanced perspective on how GPT-4o truly compares to models like Claude and Gemini across different core use cases.

General Task Performance

The benchmarks showed GPT-4o achieving elite scores above 85 on general language understanding tests like MMLU and multi-task evaluations. While Claude-3-Opus performed very strongly as well, GPT-4o demonstrated a slight edge for general language tasks.

However, qualitative feedback revealed GPT-4o can sometimes “go off the rails” and ignore important context, while Claude excelled at precisely following complex prompts and maintaining coherence. As one user put it:

“GPT-4o seems drunk and will ignore important details and just spew out some code. I’d correct it again, then again, then again, then we might have a solution. For Claude opus, I actually often trust it to rewrite my methods correctly and copy paste the new one with modifications, and it’s always correct.” (Source)

So while GPT-4o may have a slight quantitative edge, Claude could be the better choice for general tasks requiring high precision and strict adherence to prompts.

Coding

Both the benchmarks and qualitative feedback converged on Claude, especially Claude-3-Opus, being the superior model for coding and programming tasks. It outperformed GPT-4o on code generation benchmarks like HumanEval and was consistently praised by developers for its longer context window, better code reasoning, and ability to handle complex, technical prompts.

As one user summarized: “Claude opus gives very nice and thoughtful code, much much better then 4o…Claude has been better at esoteric C++ in my experience.” (Source)

However, GPT-4o was noted as being potentially stronger for specific coding tasks like analyzing multimedia inputs and visual data. But for general coding use cases, Claude appears to be the model of choice.

Large Context / “Needle in Haystack” Problems

One of Claude’s key advantages highlighted was its massive 200k token context window, compared to GPT-4’s 128k limit. This allowed Claude to better handle analysis over long documents, large codebases, and what users referred to as “needle in a haystack” problems requiring reasoning over extensive context.

As one user noted: “Claude’s context window makes it much more valuable than GPT4 if you want to feed Claude an entire codebase or a large chunk of it.” (Source)

The benchmarks also showed Claude-3-Opus outperforming GPT-4o on tests like DROP that involved complex question answering over long contexts.

So for use cases like analyzing lengthy documents, legal contracts, or large data sources, Claude-3-Opus seems to be the superior choice thanks to its context abilities.

Writing (English)

When it came to general writing tasks like content creation, copywriting, and language processing, both the quantitative and qualitative data indicated Claude held an edge over GPT models like GPT-4o.

Users consistently praised Claude’s more natural, eloquent, and human-like language outputs that avoided telltale signs of AI-generated text. As one user put it:

“Claude is much more natural in word usage as well as not repeating itself with the same phrase and using original verbiage.” (Source)

The benchmarks also showed Claude outperforming GPT-4o on summarization tasks like MGSM that require strong language generation capabilities.

So for writing-focused use cases like creative writing, marketing copy, reports and more, Claude emerges as the superior model over GPT-4o based on both empirical results and real-world feedback.

While no model is a silver bullet, this multi-dimensional analysis provides a clear-eyed view into the strengths and limitations of GPT-4o versus Claude and other language models. By leveraging the right model for the right use case, you can maximize the value and effectiveness of these AI capabilities.

Conclusion: Strategic Model Selection for Maximum Value

The analysis we’ve covered allows you to cut through the noise and understand where GPT-4o truly excels versus alternatives like Claude for your unique use cases.

For general language tasks, GPT-4o demonstrated elite performance but may lack precision. For coding and programming, the data clearly favors Claude-3-Opus thanks to its longer context window and stronger code reasoning abilities.

When working with large documents or “needle in a haystack” problems, Claude’s massive context window is a major advantage. And for writing tasks, Claude emerged as producing more natural, human-like outputs based on quantitative and qualitative evaluations.

By strategically selecting the right model per use case, you maximize effectiveness. A blended approach leveraging multiple models’ strengths is optimal. Platforms like Wielded enable combining language models seamlessly.

And if you’re spending more time rewriting and editing ChatGPT content than if you’d created it yourself, or can’t seem to get rid of that telltale “AI voice”, it may be time to level up your AI writing workflows. The AI Content Mastery course guides content teams in developing processes to create resonant, audience-centric content powered by AI — not generic, robotic outputs.

Don’t get swayed by hype. Let benchmarks and real-world feedback guide you in unlocking these AI technologies’ full potential for your needs.

Originally published at https://wielded.com.

You’re a developer or technical professional who relies on AI language models like GPT-4 to assist with coding tasks, research, and analysis. But lately, you’ve been noticing a frustrating trend — the AI seems to be getting lazier and less reliable. It provides truncated code snippets with placeholders instead of complete implementations. It omits crucial details or forgets entire sections of examples you provide. It confuses functions and parameters, invents non-existent properties, and makes silly mistakes that force you to spend more time manually fixing its output than if you’d just done it yourself from the start.

You’ve tried everything — tweaking the temperature and top-p sampling settings, adding verbose prompts with explicit instructions, even lecturing the AI sternly. But no matter what you do, it keeps dropping the ball, leaving you with half-baked, inconsistent responses that require heavy reworking. The whole point of leveraging AI was to save time and reduce your workload, but now you’re spending more effort babysitting and course-correcting than you are getting productive assistance.

The inconsistency is maddening — one moment the AI will blow you away with a brilliant, thorough implementation. The next, it will choke on a seemingly simpler task, providing an incomplete, low-effort output filled with head-scratching omissions and errors. You’ve started questioning whether the latest AI models are actually a step backwards in terms of coding reliability.

What if I told you that the root cause of the AI’s laziness often lies not with the model itself, but with the prompts and context you’re providing? Just like human communication, clear, specific, and well-framed prompts are essential for getting comprehensive, high-quality results from AI assistants. By mastering prompt engineering techniques and understanding the key factors that influence AI outputs, you can overcome the laziness and inconsistency — transforming the AI into a tireless, reliable, and invaluable coding partner.

1. Provide Exhaustive Context and Examples

AI models excel when given rich, detailed context that leaves no ambiguity about the requirements. Provide the model with:

• Thorough explanations of the task, use case, and desired output
• Multiple worked examples demonstrating the level of comprehensiveness expected
• Explicit instructions on aspects to include (e.g. error handling, validation logic)
• Supporting files, documentation, or knowledge bases it should reference

The more context you give an AI upfront, the less likely it is to make lazy assumptions or omissions.

Example Prompt

I need you to generate a complete implementation of a web scraper in Python that extracts job listings from https://www.indeed.com. The scraper should:

1. Accept search parameters like job title, location, etc.
2. Navigate to the Indeed search results page for those parameters
3. Extract the job title, company, location, salary (if listed), and link for each result
4. Save the extracted data to a CSV file

I've included an example of the desired CSV output format:

Job Title, Company, Location, Salary, URL
Software Engineer, ACME Corp, New York, NY, $120,000, https://www.indeed.com/job?id=1234
Senior Developer, Globex, San Francisco, CA, , https://www.indeed.com/job?id=5678

The scraper must implement robust error handling, including:
- Checking for and handling potential blocking from Indeed
- Retrying failed requests with exponential backoff
- Logging all errors to a file for debugging

Please provide a complete, production-ready implementation following best practices for Python web scraping. Do not use any placeholders or pseudocode - every function, class, and logic block must have its actual implementation included.

This prompt provides:

• A clear explanation of the task (building a web scraper for Indeed job listings)
• The specific requirements and expected functionality
• An example of the desired output format
• Instructions on aspects to include (error handling, validation, best practices)

By giving the model this level of context upfront, it has everything it needs to generate a comprehensive, production-ready implementation without making lazy assumptions or omissions.

Common Pitfall

A common mistake is providing too little context and letting the AI make assumptions, like:

Build a web scraper to extract job listings from Indeed.com

This open-ended prompt without any specifics will likely lead to an incomplete, barebones implementation that falls short of your actual requirements.

Pro Tip

For even more context, you can include examples of the expected code quality and structure. Add a short sample implementation demonstrating your standards for aspects like:

• Docstrings and code comments
• Variable/function naming conventions
• Separation of concerns and code organization
• Use of OOP patterns or functional programming styles

The more examples you provide upfront, the better the model can understand and match your expectations for the final output.

2. Use Directive Prompting

The phrasing and framing of your prompts significantly impacts the AI’s behavior. Use directive language that leaves no room for interpretation:

• “You must provide a complete, end-to-end implementation without any placeholders.”
• “Do not skip or omit any part of the requirements.”
• “Include comprehensive error handling, validation logic, and best practices.”
• “Your output should be ready to deploy to production without any modifications.”

You can also try “scarce” prompting by explicitly calling out what the AI should avoid:

• “Do NOT use ellipses, comments, or pseudocode as placeholders.”
• “Do NOT truncate or provide partial implementations.”
• “Do NOT omit any functions, classes, or logic specified in the requirements.”

Example Prompt

You are an expert language translator tasked with translating a JSON file from English to Spanish.

I have provided the original JSON file for you to translate. Your job is to output a NEW JSON file with an identical structure, but where ALL text fields have been translated to Spanish.

You MUST NOT use ellipses (...) or any other placeholders to truncate or omit portions of the translation. Every single text field in the JSON MUST be fully translated to Spanish in your output, no exceptions.

I will be using your translated JSON output for production purposes, so it MUST be complete and accurate. Do NOT provide a partial or low-effort translation under any circumstances.

This prompt uses very directive language emphasizing that the AI must not use ellipses or placeholders, and must fully translate every single text field to Spanish. It specifies requirements around proper formatting, structure preservation, and high translation quality.

The emphatic phrasing “MUST NOT”, “no exceptions”, “Do NOT provide partial”, etc. leaves no room for lazy interpretation — a complete, high-quality translation is the only acceptable output.

Common Pitfall

A common pitfall is giving the AI an open-ended, ambiguous prompt like:

Here is a JSON file with product listings in English. Can you translate this to Spanish for me?

With such a vague prompt, the AI is likely to provide a lazy, truncated translation using ellipses for larger text fields, under the assumption that you don’t need a comprehensive translation of the entire file.

Pro Tip

Emphasis of any kind can be very useful for highlighting important context. Beyond just directive language, you can:

• Use UPPERCASE for critical requirements
• Bold or italicize key points like this or this
• Add euphonic stresses like “You MUST, MUST, MUST…”
• Use typographic markers like ❗️ or ⚠️

Anything that makes part of the prompt stand out more is likely to increase the AI’s focus and attention on those aspects when generating its output.

For example:

You MUST ❗️❗️❗️ implement comprehensive error handling and input validation. Do NOT skip this under ANY circumstances! Your code is expected to be **production-ready** with **zero** missing functionality.

The combination of uppercasing, euphonic triplets, and typographic markers highlights the non-negotiable nature of this requirement for the AI.

Example

Let’s see how we can get GPT-4 to print out an entire JSON file where it is previously truncating the results:

• Vague Prompt with Lazy Output, vs
• Directive Prompt with Complete Output

3. Use N-Shot Prompting with Output Examples

Another powerful technique is to provide the AI with multiple examples of the desired output upfront, a method known as “n-shot prompting.” This demonstrates the exact level of detail and comprehensiveness you expect, guiding the AI to match that standard.

For example, if you need the AI to generate code following specific conventions, you can prefix or postfix your prompt with 2–3 examples showing your preferred code formatting, documentation, and organizational structure.

The AI will then learn from those examples and apply the same patterns to its own generated output.

4. Adjust Temperature and Top-P

The temperature and top-p sampling parameters control the randomness and creativity of the AI’s outputs. If you’re getting lazy or inconsistent results, adjusting these parameters can help:

• Temperature: Controls the randomness/creativity, between 0 and 2 and defaults to 1. Higher values (e.g. 0.9) make the output more random and creative. Lower values (e.g. 0.2) make it more predictable and repetitive.
• Top-P: Controls the “riskiness” of sampled tokens, defaults to 1. Higher values allow more unpredictable/riskier token choices. Lower values (e.g. 0.5) restrict to safer, more common token choices.

For coding tasks where you want comprehensive and consistent outputs, you generally want:

• Low temperature (0–0.5)
• Moderate top-p (0–0.6)

This reduces randomness/creativity in favor of more predictable, reliable outputs based on the patterns in the training data.

5. Specify Output Constraints

Another way to push the AI toward more comprehensive outputs is by setting clear quantitative constraints, such as:

• “Your list must include at least 50 Twitter accounts.”
• “Do not provide an output shorter than 2,000 words.”
• “Include at least 5 examples demonstrating different use cases.”

Example Prompt

Let’s contrast a vague prompt:

Give me a list of twitter accounts to follow on Indie Hacking

With a more constrained prompt:

Give me a list of twitter accounts to follow on Indie Hacking. There should be at least 50 accounts and give me a brief description of at least 140 characters on why I should follow each account.

The vague prompt is likely to result in a lazy, short-list of accounts without much detail whereas the constrained prompt sets clear quantitative targets the AI must aim for — at least 50 accounts, each with a 140+ character description.

6. Leverage Multiple Models for Complex Tasks

For complex, multi-step tasks that require different strengths, you can leverage the unique capabilities of multiple AI models in a consecutive prompting workflow:

1. Ask GPT-4 to outline and summarize the key steps and high-level components
2. Feed that summary to GPT-3.5 or another model better suited for detailed implementations, and have it generate the initial code/output
3. Pass that implementation back to GPT-4 for review, refinement, and finalization

This “divide-and-conquer” approach takes advantage of the distinct proficiencies of different models. GPT-4 excels at high-level reasoning, planning, and task decomposition. In contrast, GPT-3.5 has a propensity for more comprehensive, verbose outputs once given proper scaffolding.

By using GPT-4 to first distill the requirements into a clear summary, you provide the right framing for GPT-3.5 to then construct a detailed, initial implementation. GPT-4 can then review that output through its reasoning lens to refine and finalize the implementation.

Reasoning

Leverage Specialized Skills Different AI models have specialized skills based on their training data and architectures. GPT-4 is optimized for general task reasoning and multi-modal inputs. GPT-3.5 is a highly capable language model adept at generating long-form content.

By chaining their outputs, you combine their respective strengths — GPT-4’s high-level planning combined with GPT-3.5’s propensity for detail and comprehensiveness.

Cost Optimization (For Chat Completion API Users) Inference on GPT-4 is more expensive than GPT-3.5. By judiciously leveraging GPT-4 only for the planning/refinement stages, you can optimize costs while still benefiting from its high-level reasoning abilities.

7. Incorporate “Punishment” Feedback Loops

If an AI assistant continues providing lazy, low-effort, or nonsensical responses despite your best prompting efforts, you may need to implement “punishment” feedback loops.

The idea is to firmly pushback and express dissatisfaction when the AI’s output is subpar or ridiculous. This signals to the AI that its response was unacceptable and prompts it to try again from scratch, hopefully with more care and effort.

Without that pushback signal, the AI may have continued down the wrong path, providing more examples using the non-existent function under the assumption its initial response was sufficient.

Reasoning

While typically not recommended for general conversation, using terse “punishment” phrases like:

• “That’s ridiculous, try again.”
• “Nope, that output is lazy/incomplete.”
• “This is not what I asked for at all.”
• “WTF?”
• “Fxxx Off!”

Can be effective for quickly correcting an AI when it has gone off the rails. The harshness breaks the AI’s pattern of continuing with low-effort responses.

Over time, consistently punishing poor outputs while rewarding high-quality ones can reinforce the behavior you want from the AI through an operant conditioning feedback loop.

Caveats

This punishment approach should be used judiciously and is not a substitute for proper prompting. Overly harsh language may cause unintended side effects like altering the output language and tone of voice of the response.

Clear prompting should always be the first line of defense against lazy outputs.

Tools Matters

While implementing the prompting best practices outlined above can go a long way in overcoming lazy AI outputs, managing multiple prompts, personas, and language models across different tools and workflows can quickly become cumbersome.

This is where AI platforms like Wielded can help streamline your prompting process. Wielded allows you to:

1. Create Custom Prompts and Personas: Save and reuse your finely-tuned prompts as custom instructions or system prompts. The Persona feature lets you quickly switch between different profiles with unique preferences, formatting conventions, and prompt templates.
2. Leverage Multiple Language Models: Don’t be locked into a single AI like GPT-4. Many users have reported canceling their ChatGPT Plus subscriptions in favor of Anthropic’s Claude due to inconsistencies. With Wielded, you can easily connect and utilize a mix of language models like Claude, GPT-3.5, GPT-4, and Google’s PaLM/Gemini — picking the best model for each task.
3. Streamline Prompting Workflows: Implement multi-model prompting workflows, chaining outputs from different AIs using Wielded’s built-in tools. Manage feedback loops, versioning, and iteration history to continuously refine your prompts over time.

By centralizing your prompting process with tools like Wielded, you can ensure comprehensive, high-quality AI outputs every time — without the hassle of juggling multiple interfaces and disconnected prompts.

If you’re ready to take your AI prompting game to the next level, give Wielded a try.

Sign up for free trial here.

Unleash the AI’s Full Potential

At the end of the day, the latest AI language models are incredibly powerful tools that can supercharge your productivity and capabilities — when properly prompted and managed. By mastering prompting best practices like providing comprehensive context, using directive language, leveraging output examples, adjusting sampling parameters, and implementing feedback loops, you can overcome inconsistent or lazy outputs.

Tools like Wielded can further streamline the process, allowing you to centralize prompts, leverage multiple language models, and implement advanced prompting workflows. With the right prompting techniques and tools in your arsenal, you can unlock the true potential of AI assistants as tireless, reliable coding partners.

So go forth, prompt with precision, and tame the AI to be the invaluable assistant you need it to be. The path to unlocking their full capabilities lies in your prompting mastery.

You’re a developer or content marketer tasked with launching a brand new website or blog from scratch. But staring at that blank canvas is daunting — how do you go from zero content to enough pages and posts to start attracting organic traffic? The traditional approach of manually writing each piece of content one-by-one feels excruciatingly slow. You need a way to rapidly build out your site’s content architecture at scale.

The struggle is real — you understand the importance of SEO for driving sustainable traffic and growth. But creating all that content required for good SEO from day one is overwhelming. Where do you even start? What topics should you cover? How can you ensure you’re not missing any important keywords or angles?

You’ve tried various tactics like hiring freelance writers, but the output is inconsistent and doesn’t always align with your brand voice or target audience. Plus, managing all those individual content creators is a huge operational headache. You need a more systematic, scalable way to populate your site with high-quality SEO content quickly.

The pressure is on to get this new site launched and start bringing in traffic as soon as possible. But you don’t want to cut corners or resort to black hat tactics that could get you penalized by Google. You’re looking for a strategic, future-proof content generation approach that plays by the rules and sets you up for long-term success.

What if I told you there’s a way to programmatically generate hundreds of unique, high-quality content pages for your new site in just a day’s work? A way to rapidly build out your content architecture and establish topical authority through smart automation and code? This technique is called Programmatic SEO (or pSEO), and it could be the scalable content engine you need.

The Power of Programmatic SEO

At its core, Programmatic SEO is about using data and code to automatically produce a high volume of targeted content at scale. Instead of manually writing each page from scratch, you develop templates and scripts to programmatically combine data into full pages.

One of the most effective ways to implement Programmatic SEO is by creating comprehensive product/service comparison guides. These types of pages tend to be highly valuable for users researching different solutions. They also naturally incorporate a ton of relevant keywords that help with SEO.

Here’s a step-by-step process for using Programmatic SEO to rapidly generate a content library of comparison guides for your new site.

I’ll be taking you behind the curtains to show you how I generate over 200+ pages comparing various other blogging technologies and framework with one another and taking the chance to introduce wisp as an alternative.

1. Research the Competition

The first step is identifying all the major players, alternatives, and competitors in your industry/niche. Don’t just stick to the obvious big names — dig into more niche and indie offerings too. The long tail of less popular options is often underserved with quality comparison content. By being broad with your competitor research, you can uncover opportunities to capture long-tail traffic that others are missing.

const competitors = [
  'contentful', // Major enterprise player
  'sanity', // Another leading headless CMS
  'payload', // Newer headless upstart
  'contentlayer', // Niche data-fetching solution
  'contentcollection', // Indie/open-source offering
  // ...
]

As you research each competitor, start taking notes on their key features, strengths, weaknesses, and unique selling points. This will help you identify potential attributes and dimensions to structure your comparison pages around. Look for patterns in how different products differentiate themselves.

For example, you may notice that pricing model, ease of use, breadth of features, and deployment model are common areas where competitors tend to diverge. Or for blog-focused solutions, factors like native blogging capabilities, content editing experience, and SEO/discoverability features could be important points of comparison.

The more thorough your note-taking at this stage, the easier it will be to define a robust data model for capturing all the important comparison criteria in the next step.

2. Define the Comparison Data Model

With your competitor research and notes consolidated, it’s time to define a structured data model for storing all the comparison information. This data model needs to be designed for scalability, allowing you to programmatically render beautiful comparison pages from the structured data later on.

In a language like TypeScript, you can define an interface representing the different sections and properties you want to track for each technology:

interface PricingPlan { ... }

interface TechnologyComparison {
  id: string
  about: {
    link: string    
    name: string
    description: string
    logo: string
  }
  features: {
    easeOfIntegration: boolean
    easeOfEditing: boolean 
    firstClassBlog: boolean
    // ...
  }
  pricing: {
    plans: PricingPlan[]
  }
  verdict: string
}

The key sections to include are:

1. About — Basic intro details like name, description, website, and logo.
2. Features — A list of features/capabilities to compare, stored as booleans for easy toggling.
3. Pricing — The pricing plans and packages offered.
4. Verdict — A general assessment on ideal use cases and audiences.

Pro Tip: Pricing is an extremely important part of the comparison data, as users will want to easily reference and compare costs while researching solutions. If you don’t include pricing details on the page, users will likely navigate away to find that information elsewhere. By consolidating all the pricing data, you can keep users on your site through their entire research process.

The goal is to structure your data in a way that allows you to comprehensively and scalably render full comparison guides for any pair of competitors just from this model object.

3. Populate the Data

With your data model defined, you can now systematically go through each competitor and populate the respective property values into the structured data objects.

For factual details like features and pricing, you’ll need to do some research into each product’s capabilities and offerings. However, the verdict section is where you can add your own expert analysis and opinions.

const contentfulData: TechnologyComparison = {
  id: 'contentful',
  about: {
    link: 'https://contentful.com',
    name: 'Contentful',
    description: 'A leading headless CMS used by many enterprises...',
    logo: 'https://images.contentful.com/...'
  },
  features: {
    easeOfIntegration: true,
    easeOfEditing: true,
    firstClassBlog: false,
    // ...
  },
  pricing: { ... },
  verdict: 'Contentful is a robust enterprise-grade solution ideal for...'
}

This part of the process can be tedious, but the effort pays off exponentially. With each new technology you add, the number of potential comparison pages you can generate grows factorially — 2 options allows 1 comparison, 3 options allows 3 comparisons, 4 options allows 12 comparisons, and so on.

Your initial data population scales incredibly well.

When writing the verdicts, resist the temptation to make your product/service stand out as universally superior. Instead, take a differentiation approach that highlights the ideal audiences and use cases for each option based on factors like:

• Team size and resource constraints
• Simplicity vs. feature richness
• Pricing and budgets
• Specific technical requirements

For example, your verdict may suggest an open-source, markdown-based solution for solo developers prioritizing simplicity. But for larger teams, you’d highlight the collaboration and content governance features of a more robust enterprise product.

The goal is to provide an authentic, nuanced assessment that helps readers self-identify which solution is the best fit for their particular needs and context.

Pro Tip: When populated thoroughly, these verdicts become incredibly valuable research artifacts that users may reference in their internal evaluation processes. Craft them to be as insightful and unbiased as possible.

Common Pitfall: Trying to make your product/service seem like the best option for every single user. This comes across as disingenuous and undermines the value of the comparison. Embrace differentiation based on real use case segmentation.

4. Generate Comparison Guides

With all your data prepped, you can now programmatically generate full comparison guides for every possible pair of competitors. I use Next.js Server Component to render the page dynamically.

In a Next.js React app, you can leverage the app/ directory conventions and file-based routing to automatically create comparison pages based on path parameters:

// app/compare/[id1]/[id2]/page.tsx
import { TechnologyComparison } from './data'

export default async function Page({ params }: { params: { id1: string, id2: string }}) {
  const technology1 = findTechnology(params.id1)
  const technology2 = findTechnology(params.id2)

  // Render the two technologies for comparison...
}

This code sets up a dynamic route segment that will automatically render a new page for every unique id1 and id2 combination that gets hit, like /compare/contentful/sanity, /compare/sanity/payload, etc.

Within each page, structure the content using components for different comparison sections: overview, features, pricing, verdict, and calls-to-action. Intersperse these components with persuasive content blocks that reiterate the value of your product/service as an alternative solution.

Pro Tip: For the pricing component, be sure to implement any pricing logic or calculations in the UI code itself using the raw data from your model. This allows you to easily tweak things like displaying annual vs monthly costs, discounts, etc.

Common Pitfall: Forgetting to include calls-to-action and pathways for users to explore your own product/service offering after they’ve reviewed the comparison guides.

The beauty of this programmatic approach is that it allows you to rapidly build out a content architecture with exponential returns on your initial data input.

Every new competitor you add increases the size of your comparison guide library exponentially (or more accurately factorially).

5. Automate SEO Optimizations

To maximize the SEO impact of all these new content pages:

Navigation

Ensure the comparison pages are linked and navigable from your primary website structure. Add a “Compare” section to the main nav that deep links to other comparison pages. Even better, create a dedicated directory page that lists out and links to every single comparison page for easy navigation.

// app/compare/page.tsx
export default function Page() {
  return (
    <div>
      <h1>Compare Different Solutions</h1>
      <div>
        {comparisonData.map((data) => (
          <div key={data.id}>
            <Link href={`/compare/wisp/${data.id}`}>
              <h2>{data.about.name}</h2>
              <p>{data.about.description}</p>
            </Link>
          </div>
        ))}
      </div>
    </div>
  )
}

This directory page can dynamically render out links to every comparison page by mapping over your main comparisonData array. Now users and web crawlers have an easy entry point for discovering all your other comparison content.

Pro Tip: When designing your site navigation, prioritize creating navigation pathways that don’t just dead-end on comparison pages. Allow users to easily continue their journey and explore your product/service offering.

Sitemaps

Automatically generate and submit a sitemap to Google, Bing, etc. so they can easily discover and index all the new content.

Using Next.js sitemap.ts special file, you can generate the sitemap programmatically as well:

// app/sitemap.ts
export default function sitemap(): MetadataRoute.Sitemap {
  const urls: MetadataRoute.Sitemap = []
  for (let i = 0; i < comparisonData.length; i++) {
    for (let j = i + 1; j < comparisonData.length; j++) {
      urls.push({
        url: `${appConfig.baseUrl}/compare/${comparisonData[i].id}/${comparisonData[j].id}`,
        lastModified: new Date(),
        changeFrequency: "monthly",
        priority: 0.7,
      })
    }
  }
  return [...urls]
}

6. Index your content

With your comparison content generated and optimized for SEO, the final step is to ensure it gets properly indexed by search engines like Google.

First, submit your new sitemap to Google Search Console for indexing. While you could have a single massive sitemap listing every URL, it’s better to take a hierarchical approach. Include the sitemap for your new comparison pages in your main content sitemap so it’s easily discoverable.

<!-- main-sitemap.xml -->
<?xml version="1.0" encoding="UTF-8"?>
<sitemapindex xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  ...
  <sitemap>
    <loc>https://www.example.com/sitemap-compare.xml</loc>
  </sitemap>
</sitemapindex>

This hierarchical structure keeps things organized and easier to manage as you continue expanding your content.

Next, manually submit your new sitemap(s) through Google Search Console to expedite indexing of the new URLs. However, don’t stop there!

To really accelerate indexing, also submit the main directory page that links to all your comparison pages (e.g. /compare). Since this acts as an entry point to the rest of the content, getting it indexed quickly allows Google's crawlers to easily discover and follow the links to the other comparison URLs from there.

By combining an optimized sitemap strategy with manual indexing submissions, you can rapidly get your entire programmatically-generated content library indexed and ranking in a matter of weeks rather than months.

7. Results

To show just how powerful this Programmatic SEO approach can be, here’s a screenshot from my Google Search Console just 3 days after launching my new comparison content:

While these initial impressions and rankings are promising, it’s important to understand that it takes Google time to fully assess the quality of new content. The traffic and rankings you see in the first couple of weeks are just the beginning.

Typically, you’ll want to allow 3–6 months for Google to properly crawl, index, and understand the topical relevance of all your new programmatically generated pages. During this time, rankings and traffic can fluctuate as Google’s algorithms figure out how to appropriately serve your content to users.

The key is to be patient and allow the process to play out. Keep producing great content, optimize for SEO best practices, and let Google’s systems do their job. The early wins are just a preview of the sustainable, passive traffic stream this approach will create for your site long-term.

Now, your turn!

Whether you’re launching a brand new website/blog or looking to reinvigorate an existing one with fresh SEO content, Programmatic SEO is a game-changing strategy. By combining data, code, and templates, you can programmatically produce hundreds of unique, high-quality pages at scale.

The key is taking the time upfront to thoroughly research your competitors, define a robust data model for capturing all the important comparison points, and design persuasive page templates that guide users through the content. With those foundational pieces in place, you can rapidly spin up an entire library of mind-reading comparison guides.

Combine that powerful content with smart navigation, sitemaps, and indexing tactics, and you’ll be driving organic traffic before you know it. And since everything is code-based, it’s easy to maintain and expand your content architecture over time as your product evolves.

While it requires some upfront work, the scalable returns on your initial investment make Programmatic SEO absolutely worth it, especially for any business heavily reliant on organic traffic. It’s a way to systematically produce user-value content that ranks, converting researchers into customers through a thoughtful, differentiated approach.

Originally published at https://www.wisp.blog.

So you’ve started a blog and you’re excited to share your work with the world. But you also don’t want to be “that person” who constantly spams their social media feeds with endless self-promotion. We’ve all had that Facebook friend who seems to post 10 times a day about their new business or blog, and it gets old fast.

The good news is that there are plenty of ways to effectively promote your blog and grow your audience without relying on your existing social circle or resorting to spammy tactics. Here are some strategies to try:

Optimize for Search Engines

One of the most sustainable ways to drive traffic is through search engine optimization (SEO). Do keyword research to find topics your audience searches for, then create high-quality content targeting those keywords. Use on-page SEO like optimizing titles, headings, and meta descriptions.

Pro Tip: Use a tool like Google’s Keyword Planner or Ahrefs to identify low-difficulty keywords with high search volume to target.

Common Pitfall: Keyword stuffing. Don’t overdo it — Google is smart and will penalize you for unnaturally forcing in too many keywords.

Engage on Other Blogs and Online Communities

Instead of just promoting your own blog, spend time engaging on other relevant blogs and online communities. Leave thoughtful comments, answer questions, and participate in discussions. This will get your name out there and drive some of that traffic back to your site.

Pro Tip: Use a tool like BuzzSumo to find the most popular content and influencers in your niche to engage with.

Common Pitfall: Only dropping spammy links and self-promotion. Focus on providing value first through your comments and engagement.

You know you need to deeply understand your audience to create successful content. But actually getting inside their heads feels impossible. How can you really know what they’re thinking and what they truly want? The Sales Safari Mastery guide shows you how to essentially become a fly on the wall inside your audience’s minds by lurking in their online spaces and taking meticulous notes on their authentic thoughts and behaviors.

Guest Post on Relevant Sites

Identify blogs and publications with an audience you’d like to reach. Pitch ideas for guest posts that would be a good fit for their site. When done right, guest posting is a win-win — you get exposure, and the other site gets free content.

Pro Tip: Research the site’s guest posting guidelines and audience to craft a pitch that’s tailored to them. Make it as easy as possible for them to say yes.

Common Pitfall: Guest posting on low-quality sites with no traffic. Focus on sites with a strong, relevant audience.

Leverage Social Media Strategically

While you don’t want to spam your friends’ feeds, you can still use social media strategically. Share your best content at optimal times when your followers are most active. Use hashtags to reach new audiences. Engage with others by commenting on and sharing their posts. Run ads to specific interest groups.

Pro Tip: Use a tool like Buffer or Hootsuite to schedule your social media posts for ideal timing. Experiment with different posting times to see what works best.

Common Pitfall: Posting too much self-promotional content. Follow the 80/20 rule — 80% should be other valuable content, only 20% directly promoting your own.

You know having an engaged audience is critical for getting feedback and finding customers. But getting eyeballs on your work feels impossible. The guide to growing your Twitter following shows you how to realistically build an audience of over 1,000 engaged, relevant followers on Twitter in just a few months by strategically inserting yourself into existing communities.

Build an Email List

One of the most valuable assets is an email list — a direct line to people who’ve opted in to receive your content. Offer visitors an incentive like a free ebook or email course in exchange for their email address. Send a weekly newsletter highlighting your latest and best content.

Pro Tip: Use exit-intent popups and banners to capture email addresses from visitors who are about to leave your site.

Common Pitfall: Buying an email list. This is considered spam. Only send emails to those who have explicitly opted in.

Create Shareable Content

If you want your content to spread through shares and word-of-mouth, create things that are truly shareable. In-depth guides, infographics, videos, or highly emotional/controversial content tend to get shared most.

Pro Tip: Include click-to-tweet links with pre-written tweets highlighting your best insights and stats to make it easy for readers to share.

Common Pitfall: Not giving people a reason to share. Ask yourself “Why would someone want to share this?” when creating content.

Be Patient and Consistent

Building a successful blog is a long-term game. It takes time, effort, and consistency to grow an audience from scratch. Stick to a regular publishing schedule, keep promoting through various channels, and don’t get discouraged.

Pro Tip: Analyze what’s working using Google Analytics. Double down on the channels and content types that drive the most traffic and engagement.

Common Pitfall: Giving up too soon. Stick with it — audience growth often starts slowly before compounding over time.The key is to focus on creating great content and promoting it in a way that provides value rather than coming across as spammy or self-promotional. With patience and the right strategies, you can grow your blog’s audience without annoying your social circle in the process.

Originally published at https://wispcms.com.

You know you need to deeply understand your audience to create successful products and marketing. But actually getting inside their heads feels impossible. How can you really know what they’re thinking and what they truly want? And how do you find the secret ingredient that makes your audience sit up, take notice, and cling to your every word?

You’ve tried all the typical tactics — customer surveys, focus groups, even mining reviews and comments. But people often struggle to articulate their deepest desires and pain points. Or they tell you what they think you want to hear instead of their unvarnished truth.

It’s incredibly frustrating watching your “audience-informed” content and offers fall flat. You’re putting in all this work trying to give them what you think they want, but it keeps missing the mark. If you can’t crack the code of what motivates them at a primal level, your business is going to stay stuck.

What if there was a way to essentially become a fly on the wall inside your audience’s minds? To eavesdrop on their unfiltered conversations and observe their authentic thoughts, struggles, and behaviors in their native environment?

Be a Jedi Mind Reader with Sales Safari

That’s exactly what the Sales Safari technique allows you to do. By lurking in the online “watering holes” where your prospects gather, you can take meticulous notes on how they communicate their pains, use insider jargon, and make recommendations to each other. This raw data gives you an unprecedented window into their psyches.

In this mega-guide masterclass, you’ll get the complete blueprint for conducting your own Sales Safari to deeply understand your audience on a profound level. Pioneered by Amy Hoy of Stacking the Bricks, Sales Safari is the key to:

• Identifying the perfect online forums, communities, and discussion spaces (the “watering holes”) where your prospects freely open up
• Taking meticulous notes that capture their authentic pains, jargon, and recommendations in their own words
• Analyzing your notes to extract key themes, insights, and opportunities around their unmet needs
• Mapping out and crafting persuasive “e-bomb” content that makes your audience think “Whoa, it’s like you plucked this right from my brain!”
• Strategically delivering those e-bombs to attract, nurture, and build insatiable demand among your prospects
• Leveraging your new deep audience understanding to create wildly successful paid products and services

How Sales Safari Works?

The key to effective marketing is understanding your audience so intimately that you can read their minds and give them exactly what they want before they even realize they want it. This profound audience understanding is the superpower of Sales Safari.

By observing your prospects’ natural online conversations and behaviors, you can extract priceless insights into their pains, jargon, recommendations, and unmet needs. With this data, you can craft educational content marketing bombs (“e-bombs”) that make your audience think “Whoa, it’s like you plucked this right from my brain!”

When your e-bombs demonstrate this level of understanding, your audience will:

• Trust you as the go-to expert who “gets” them
• Get excited about the novel insights you provide
• Become willing to invest their time, social capital, and money into whatever you offer

Mastering Sales Safari allows you to deeply connect with your audience, build insatiable demand for your perspective, and ultimately position yourself as an authority worth paying attention to.

This mega-guide masterclass will give you a start-to-finish blueprint for conducting Sales Safari, creating mind-reading e-bombs from your findings, and delivering those e-bombs back to your audience to nurture them into raving fans and customers.

We’ll break down the process into four key stages:

• Stage 1: Gather Safari Data
• Stage 2: Analyze Safari Data
• Stage 3: Create E-Bombs from Data
• Stage 4: Deliver E-Bombs to Audience

Each stage will include step-by-step instructions, illustrative examples, common pitfalls to avoid, and pro tips for mastering the techniques. You’ll walk away from this guide knowing exactly how to:

• Identify the perfect online “watering holes” for observing your prospects
• Take meticulous notes that capture their pains, jargon, and recommendations
• Analyze your notes to extract key themes, insights, and opportunities
• Map out and craft persuasive, mind-reading e-bomb content
• Strategically publish and promote your e-bombs to attract and nurture your audience
• Leverage your e-bomb success to ultimately sell products/services

Whether you’re an entrepreneur, freelancer, marketer, or business owner, Sales Safari and e-bombs are the keys to understanding your audience’s deepest desires and giving them exactly what they want. The connection and trust you build through this process is the foundation for creating anything they’ll pay for.

Let’s dive into Stage 1 and get started gathering priceless audience insights through Safari!

Stage 1: Gather Safari Data

The first stage is to observe your prospects by lurking in their online “watering holes” and taking meticulous notes on how they communicate their pains, use jargon, and make recommendations. This raw data will fuel all your future audience understanding.

Step 1: Identify Watering Holes

Watering holes are any online locations where your prospects gather and have natural conversations amongst themselves, free from sales agendas.

This could include:

• Niche forums/message boards (e.g. makermoney.com, chowhound.com)
• Facebook/LinkedIn groups (e.g. Keto Recipes, Social Media Marketers)
• Reddit communities (e.g. r/woodworking, r/webdev)
• Amazon product reviews (e.g. for cameras, cookware, software)
• Blog comment sections (e.g. copyblogger.com, nngroup.com)
• Slack communities (e.g. Tropical MBA, Dynamite Circle)
• Email lists/newsletters (e.g. Hustle Crew, Brain Food)
• Q&A sites like Stack Overflow, Quora

The best watering holes have active, ongoing dialogues rather than just one-off questions and answers. You want places your prospects feel comfortable openly discussing their struggles, curiosities, and experiences without filters.

Example: Let’s say you want to create products for beginner rock climbers. Potential watering holes could include:

• reddit.com/r/climbing
• reddit.com/r/climbingpartners
• mountainproject.com forums
• REI.com gear reviews
• Climbing Narc newsletter
• Local climbing gym message boards

You’ll want to identify 5–10 potential watering holes to start with for your particular audience of interest.

Step 2: Set Up Safari Notebook

For each watering hole thread you explore, you’ll create a new document with the following sections:

URL:

Date:

Pain:

Jargon:

Recommendations:

Having a consistent format will keep your notes organized as you gather more and more data over time. You can use a simple text file, Google Doc, Evernote, or any other method you prefer.

Step 3: Observe and Take Notes

Spend 15–30 minutes carefully reading through the full thread conversation. As you read, jot down any observations that fall into the pain, jargon, and recommendations categories:

Pain — Note any statements, outcomes, or behaviors that indicate your prospects are experiencing struggles, frustrations, or unmet desires. Pain can be subtle, like procrastinating on a task or constantly seeking workarounds.

Example Pain Notes:

• “Tweaked my knee bouldering last week and the pain just won’t go away.”
• “Forced to take time off climbing until my meniscus tear heals, which could be months.”
• “Rehabbing my knee has been way slower than I hoped and it’s really demotivating.”

Jargon — Record any specialized terminology, insider phrases, or common words used in unconventional ways by this audience. Jargon creates tribal signals that build trust.

Example Jargon Notes:

• “Bouldering”
• “Meniscus tear”
• “Proj” (short for ‘project’ climb)
• “Knee bar”
• “Gaston”

Recommendations — Document any advice, tips, product recommendations (positive or negative), or suggested actions made by your prospects.

Example Recommendations Notes:

• “I used a knee brace when I had meniscus issues and it helped a lot for prevention and stability.”
• “Definitely lay off any knee-bearing moves like knee bars until it’s 100% healed.”
• “Look into RICE (rest, ice, compression, elevation) and some PT exercises.”
• Don’t worry about being comprehensive at first — just note anything interesting that could reveal insights into how your prospects think and what they need.

Common Pitfall: Feeling lost or overwhelmed in the beginning is normal! Safari is an analytical skill that takes practice. Keep an open mind and don’t self-edit your notes.

Pro Tip: Use shorthand, abbreviations, and bullet points to capture notes quickly as you read through threads. You can go back and expand on things later.

Stage 2: Analyze Safari Data

Once you have raw notes from several watering hole threads, it’s time to look for patterns and extract key insights. The more data you gather, the clearer the patterns will become.

Step 1: Review Pain Notes

Comb through your pain observations and look for:

• Frequently mentioned struggles or frustrations
• Intense emotional language indicating deep pain
• Behaviors that indicate underlying dissatisfaction (procrastinating, seeking shortcuts, expressing confusion/uncertainty)
• Unmet desires or aspirations they wish to achieve

Identify the core pains your audience faces. Don’t just look for explicit complaints, but read between the lines for implied or oblique pains.

Example Pain Analysis:

From the rock climber notes, some core pains appear to be:

• Injuries preventing them from climbing and progressing
• Lack of knowledge around proper rehab/recovery for climbing injuries
• Frustration and demotivation from having to take time off
• Wanting to get back to climbing at their previous level as soon as possible

Step 2: Study Jargon Notes

Your jargon notes reveal the vocabulary and tribal language of your audience. Look for:

• Recurring specialized terms they use
• Common words/phrases used in unconventional ways
• Coded language only insiders would understand

Mastering their jargon is critical for communicating in a way that builds instant rapport and trust when you share content with them later.

Example Jargon Analysis:

For rock climbers, important jargon includes:

• Bouldering (climbing short, rope-less routes)
• Proj (slang for ‘project’ — a climb they’re working on sending)
• Knee bar (using bent knee to make climbing hold)
• Gaston (using the back of the hand as a grip)
• Meniscus tear (common climbing knee injury)

Step 3: Examine Recommendations

Your recommendations notes provide clues about:

• Products/services your audience currently uses or avoids
• Existing solutions they perceive as incomplete or ineffective
• Advice they share with each other about particular approaches

This data can uncover opportunities to create a product or e-bomb that improves upon the current limited solutions.

Example Recommendations Analysis:

Some recommendations made for dealing with climbing injuries:

• Knee braces for prevention/stability
• Avoiding knee-bearing moves like knee bars
• RICE method (rest, ice, compression, elevation)
• Physical therapy exercises

Potential opportunities:

• Comprehensive rehab/prehab guides for climbers
• Knee braces designed specifically for climbers
• Preventative knee exercise programs

Step 4: Identify Themes and Patterns

As you review your categorized notes, look for overarching themes that connect different pains, jargon, and recommendations. These themes represent the core challenges and missed opportunities in your market.

For example, you may notice a theme around your prospects feeling frustrated and confused by certain common practices in your industry. Or struggling to achieve a particular outcome due to lack of guidance. Identifying these key themes allows you to create e-bombs and products providing clarity.

Example Themes:

For rock climbers, some potential themes:

• Climbers feeling defeated by injuries and unsure of proper recovery
• Lack of rehab resources tailored specifically to climbers
• Wanting preventative exercises to avoid common climbing injuries
• Confusion around modifying training during rehab periods

Common Pitfall: Having too little data to confidently pinpoint patterns. Gather more Safari notes from additional watering holes before moving to Stage 3.

Pro Tip: Use sticky notes, index cards, or spreadsheets to categorize and visually organize your pain, jargon, and recommendation notes as you analyze for themes.

Stage 3: Create E-Bombs

Now that you intimately understand your audience’s pains and how they communicate, it’s time to create educational, mind-reading content that “bombs” them with value and solutions.

Step 1: Choose E-Bomb Topic

Review your identified themes, pains, jargon, and recommendations. Look for a specific pain point or unmet need that your e-bomb can provide clarity around.

Example E-Bomb Topic:

For rock climbers, a hot topic could be:

“How to Rehab a Climbing Knee Injury and Prevent Re-Injury”

This allows you to tap into the pains around injuries, lack of knowledge, and desire for preventative guidance.

Step 2: Map Out E-Bomb Sections

Effective e-bombs have a narrative arc that begins by articulating the audience’s pain in their own words, then progresses through providing insights and solutions:

1. The Opening Pain Punch: Use your audience’s verbatim words/jargon to describe their painful situation so vividly that they immediately feel understood.

Example Opening:

You tweaked your knee while bouldering on your proj last weekend, and that nagging pain just won’t go away. Taking time off climbing is the last thing you want, but that meniscus tear needs to heal before you risk re-injuring it worse…

2. The Empathetic Exploration: Expand on the nuances of the pain, showing deeper layers of the struggle that they may not overtly express, but you know they experience.

Example Exploration:

While you’re trying to stay positive, the thought of missing out on prime climbing season is really demotivating. You’ve worked so hard this year, making solid progress and finally feeling like you’re breaking through to the next level. But now you’re forced into being a gumby again, watching your hard-earned strength and technique get rusty as the weeks off add up…

3. The Aha! Insight: Provide a key paradigm shift or new perspective that reframes the pain in a novel, enlightening way.

Example Insight:

What if I told you that with the right rehab approach, you could use this enforced downtime to not just recover from your injury, but come back stronger than before? By focusing on mobility, stabilization, and preventative exercises, you can rebuild your climbing body in a way that bulletproofs you against re-injury…

4. The Recommendations: Give specific advice, tips, or strategies for overcoming or mitigating the pain you outlined.

Example Recommendations:

Here are 5 steps for rehabbing a climbing knee injury the smart way:
1. Optimize the RICE protocol for climbers…
2. Perform these mobility exercises to restore full range of motion…
3. Strengthen these muscle groups to stabilize the knee joint…
4. Slowly reintroduce climbing using this progression plan…
5. Incorporate these preventative exercises into your warm-up…

Step 3: Write E-Bomb

Using the above structure as an outline, write out your full e-bomb content. Generously use the jargon you gathered, provide real-world examples that mirror your prospects’ experiences, and sprinkle in recommendations you overheard.

The more you demonstrate a deep, mind-reading level of understanding of their realities, the more your e-bomb will resonate.

Example E-Bomb:

[Opening Pain Punch]

You tweaked your knee while bouldering on your proj last weekend, and that nagging pain just won’t go away. Taking time off climbing is the last thing you want, but that meniscus tear needs to heal before you risk re-injuring it worse. You’re already feeling rusty and weak just thinking about it.

[Empathetic Exploration]

While you’re trying to stay positive, the thought of missing out on prime climbing season is really demotivating. You’ve worked so hard this year, making solid progress and finally feeling like you’re breaking through to the next level. But now you’re forced into being a gumby again, watching your hard-earned strength and technique get rusty as the weeks off add up.

Even worse, you have no idea if you’re doing the right things for recovery. You’ve iced and rested per the usual RICE protocol, but that knee still doesn’t feel right. And you’ve heard too many stories of climbers rushing back too soon only to immediately re-injure themselves in a way that shelves them for months.

[Aha! Insight]

What if I told you that with the right rehab approach, you could use this enforced downtime to not just recover from your injury, but come back stronger than before? By focusing on mobility, stabilization, and preventative exercises, you can rebuild your climbing body in a way that bulletproofs you against re-injury and actually unlocks new levels of performance.

[Recommendations]

Here are 5 steps for rehabbing a climbing knee injury the smart way:

1. Optimize the RICE protocol for climbers. While standard RICE works for most injuries, we need some modifications for our sport. For ice, use an ice cup to drive deeper into the joint. For compression, use a knee brace that still allows range of motion…

2. Perform these mobility exercises to restore full range of motion. Regaining full flexibility around the knee is critical for preventing re-injury. Movements like knee sculpting, quad smashing, and meniscus flossing will get you there…

3. Strengthen these muscle groups to stabilize the knee joint. Weak stabilizers like VMO, glute med, and the entire posterior chain are ground zero for most climber knee issues. Add these exercises to reinforce those areas…

4. Slowly reintroduce climbing using this progression plan. Going from 0 to 100% crimping is a recipe for re-injury. Follow this 6-week plan to gradually ramp up load and intensity on the knee…

5. Incorporate these preventative exercises into your warm-up. An ounce of prevention is worth a pound of cure. Make these activation moves part of your pre-climb routine to stay bulletproof…

[Close]

By taking a full-body, prehab-based approach to your knee rehab, you’ll not only recover fully from this injury, but you’ll come back stronger, safer, and with more potential than before. No more time off for knee issues — get back to sending in no time!

Common Pitfall: Trying to promote or sell something in the e-bomb itself. E-bombs are for building trust and positioning through giving value first.

Pro Tip: Read your e-bomb out loud to yourself as a gut-check for authenticity. Does it really sound like something your audience would say/think? If not, go back and refine it with more of their jargon and real-talk.

Stage 4: Deliver E-Bombs

With your persuasive e-bomb created, it’s time to deliver it in a way that attracts and nurtures your prospects.

Step 1: Identify Delivery Channels

There are several potential channels for sharing your e-bomb:

• Your blog/website
• Relevant watering holes (forums, groups, etc.)
• Email list
• Social media
• Guest posting on other blogs
• YouTube/podcasting
• Free PDF downloads

The right channels will depend on where your particular audience hangs out online and what content formats they prefer.

Example Delivery Channels:

For the rock climbing audience, potential delivery channels include:

• Posting the full e-bomb on your rock climbing blog
• Sharing a snippet version in the reddit.com/r/climbers subreddit
• Recording a video version for your YouTube channel
• Offering a free PDF download of the rehab plan via an email list
• Pitching a written version as a guest post to popular climbing sites

Step 2: Establish Posting Rhythm

Don’t just blast out one e-bomb and move on. Create a consistent cadence for sharing educational content that keeps feeding your audience’s desires for more insights.

A good rule of thumb is to publish one high-quality e-bomb per week across your channels. This rhythm demonstrates your expertise while allowing you to observe how your audience responds.

Example Posting Schedule:

• Monday: New blog post with full e-bomb
• Tuesday: Snippet on Reddit with link to blog post
• Wednesday: Email list update highlighting the e-bomb
• Thursday: Guest post goes live on another site
• Friday: YouTube video summary of key points

Step 3: Interact and Nurture

As you share more e-bombs, your audience will start to see you as the go-to expert for certain topics. Some will inevitably reach out with follow-up questions or testimonials about how your content helped them.

Be sure to continue the conversation! Respond promptly, offer additional advice/support, and keep nurturing these engaged prospects. They are primed to become your first customers once you create an offer.

Example Interaction:

Someone comments on your rock climbing knee rehab e-bomb:

This is awesome, thanks for sharing! I’m about 4 weeks out from my meniscus tear and have been struggling to find good sources for climber-specific rehab. A couple quick questions if you don’t mind:

1. What are some good exercises for regaining full flexion?

2. How do you know when you’re ready to start easy bouldering again?

Your reply:

Thanks for the great questions! For regaining flexion, something like the ‘ped n’ strap’ mobilization can be really helpful… [provide specific tips]

As for when to start bouldering again, I’d follow a progression like… [lay out plan]

Let me know if you have any other questions come up! Wishing you the best of luck with your recovery.

By taking the time to directly engage with your audience, you continue building credibility while learning about their biggest sticking points. This helps refine your future e-bomb topics and content.

Step 4: Analyze and Refine

Pay close attention to which e-bombs generate the most engagement, shares, and positive reactions from your audience. This feedback loop allows you to double down on the content that truly resonates.

Over time, you can refine your e-bomb creation process, delivery channels, and nurturing strategies for maximum effectiveness based on what moves the needle for your specific audience.

Example Analysis:

You notice that your knee rehab e-bomb got 5x more engagement than your previous e-bomb about beginner climbing techniques. A few potential reasons:

• Knee injuries are a more painful and urgent problem to solve
• The rehab topic positioned you as a unique expert
• The narrative style and specificity resonated better

From this data, you might decide to double down on creating more injury prehab/rehab e-bombs. You could also analyze what made the narrative and specificity so effective to improve your writing style.

Additionally, you could look at which delivery channels got the most eyeballs on that e-bomb. For example, if Reddit drove very little traffic while your blog/email list did, you could deprioritize Reddit for that audience.

Common Pitfall: Expecting instant results or getting discouraged early on. It takes time to build the momentum and nurture the trust required for monetization. Stay consistent!

Pro Tip: Set up a simple tracking system for monitoring metrics like website visits, e-bomb shares, email list growth, etc. Having these numbers will help you analyze what’s working or not.

Bonus: Monetizing Your E-Bomb Success

The entire Sales Safari and e-bomb process is designed to build a tribe of prospects who avidly trust and engage with your content. With that connection established, you’ll be able to successfully sell them products or services that provide even more value.

Step 1: Identify Commercial Opportunities

Review your most popular e-bombs and the specific pains/struggles they addressed. Look for opportunities to create a premium paid product that explores the topic in greater depth.

Example Commercial Opportunity:

For the rock climbing audience, the knee rehab e-bomb was a smash hit. Potential paid product ideas:

• Comprehensive video course: “Bulletproof Knees for Climbers”
• Physical book: “Climb Injury-Free: The Complete Guide”
• Ebook: “7 Weeks to Rebuild After a Climbing Knee Injury”
• In-person workshop: “Rehab and Prehab for Climbers” seminar

The key is providing more strategic value than the free e-bomb could.

Step 2: Pre-Sell and Get Feedback

Before going all-in on creating your product, get feedback from your engaged audience. You can:

• Send a “pre-sale” email survey to your list
• Post polls/questions in your watering holes
• Get on a call with some of your most active e-bomb participants

Validate there is actual demand, get input on the ideal format/content/pricing, and have them effectively “co-create” the product with you.

Step 3: Create Paid Product

Using all the feedback and data you’ve gathered, map out and create your premium paid product. Focus on making it as comprehensive, effective, and transformative as possible.

Step 4: Sell to Your Audience

With your credibility as the go-to expert firmly established through your e-bombs, you now have an eager audience to sell to! Some selling strategies:

• Offer the product for sale after delivering a popular e-bomb
• Promote the launch to your email list
• Run a paid advertising campaign re-targeting e-bomb visitors
• Collaborate with other creators in your niche for cross-promotions
• Publish a free sample/video to drive sales

The more value, authority, and trust you’ve built through your e-bombs, the more successful your product launch will be.

Step 5: Nurture Customers

The sales cycle doesn’t end after someone becomes a customer! Your goal is to delight them and provide transformative results through your product. Nurture them by:

• Offering customer-only resources and bonuses
• Creating a community for accountability and connection
• Gathering feedback for improving your product
• Promoting your next premium product to them first

By continuing to nurture your customers, you’ll build a base of raving fans who buy everything you create while generating renewable sales through word-of-mouth.

The Sales Safari process creates a perpetual cycle of audience growth, nurturing, and monetization. With each new free e-bomb, you’ll attract more prospects. And with each new premium product, you’ll convert more customers who’ll eagerly await your next creation.

Mastering this system allows you to systematically and sustainably grow an audience-first business that prints money by giving your tribe exactly what they want before they even realize it.

Closing Thoughts

Sales Safari is the ultimate superpower for truly getting your audience at the deepest level. By observing their unfiltered conversations in their native online habitats, you can extract priceless insights into their authentic thoughts, struggles, and desires. This profound understanding allows you to create mind-reading content and offers that make them think “Whoa, it’s like you plucked this right from my brain!”

The connection and trust you build by giving your tribe exactly what they want becomes the foundation for a sustainable, audience-first business. One where your credibility as the go-to expert allows you to print money on repeat by delivering more and more transformative value.

So get ready to embark on your own safari. Come back with a treasure trove of insights that will revolutionize how you connect with and nurture your audience. This is how you position yourself as an authority worth paying attention to.

Deepen Your Knowledge:

Sales Safari in a Nutshell

A quick intro to the Sales Safari concept. Skip to the cheat sheets at the end for a list of great search terms to identify watering holes.

Amy Hoy’s Sales Safari Demo

While a decade old, this video from Sales Safari pioneer Amy Hoy illustrates the entire process of studying an online watering hole for endless content gold. I’ll reference it again, but watch it now to fully grok the technique.

30x500 Academy

The full 30x500 curriculum with bite-sized, no-fluff video lessons from Amy Hoy on taking action and getting real results through techniques like Sales Safari.

Wisp Blog

As the founder of Wisp — a CMS for elevating the blogging experience on Next.js websites — I’ve seen the power of Sales Safari firsthand. I regularly share insights and resources on my blog aimed at helping marketers and developers leverage content more effectively.

Originally published at https://wispcms.com.

Blogs on Next.js SUCKS!!!

Let’s be real here, managing a blog on your Next.js website can be a total pain in the ass, especially if you’re an indie hacker or a bootstrapped startup. I’ve been there, done that, and I’ve got the battle scars to prove it.

Working with MDX files was supposed to be a breeze, but it quickly turned into a nightmare. Generating sitemaps, creating blog lists, and handling image uploads and hosting became tasks that I dreaded.

And don’t even get me started on the lack of a user-friendly interface for non-developers to create and edit blog posts. Unless you were fluent in markdown, contributing to the blog sucks big time. I can’t tell you how many times I’ve had to waste precious developer hours pushing code changes just to fix a simple typo on a blog post. Talk about inefficient!

And let’s not forget the nightmare of hosting images within the same repository as the codebase. Code and fixed assets in a single place? Let’s talk about being disgusting.

wisp = less frustration & more content

That’s why I set out to build wisp. I realized that if I wanted to maintain my sanity, I needed to create a solution that would address these pain points once and for all. Enter wisp, a headless CMS designed specifically for indie hackers and startups who want to add a blog to their existing Next.js website without the hassle.

With wisp, I set out to create a solution that would make managing a blog easy. At its core, wisp offers a Medium-like editor, making it a breeze for non-developers to create and edit content without having to learn a single line of code. No more wrestling with MDX files or deciphering cryptic markdown syntax — just a clean, intuitive interface that even your grandma could use.

But that’s just the tip of the iceberg. wisp also provides fine-grained controls for SEO optimization, ensuring that your blog posts are optimized for search engines and can be easily discovered by your target audience. Because let’s face it, what’s the point of creating amazing content if no one can find it?

And when it comes to images, wisp takes care of uploading and serving them via a CDN, eliminating the need to worry about hosting or remembering complex markdown syntax. No more headaches, no more frustration — just a seamless experience that’ll make you wonder why you ever did it any other way.

Try it out?

After two weeks of dedicated work (and maybe a few extra cups of coffee), I’m thrilled to showcase the first blog post written and served from the wisp CMS. This very post you’re reading is powered by wisp and served through a Next.js 14 app using the App Router.

If you’ve experienced similar frustrations with managing a blog on your Next.js website, I invite you to sign up for early access to wisp. In the coming week, we’ll be opening the doors to a limited number of users who want to take wisp for a test drive and provide valuable feedback.

No more pulling your hair out, no more cursing at your computer screen, and no more wasting developer hours on trivial tasks — just a smooth, seamless experience that’ll make you wonder why you ever did it any other way.

So, what are you waiting for?

Sign up for early access, and let’s make content creation a whole lot less frustrating.

Originally published at https://wispcms.com.