The Importance of Context in Prompting

We’ve spent the last year obsessing over prompt engineering. And for good reason — how we ask the question impacts the output we get from GenAI. But here’s the truth most people haven’t realized yet: Prompt engineering is just the tip of the iceberg. In the not-so-distant future, you won’t even see prompts anymore. Most of them will be buried in buttons, voice assistants, and agents running behind the scenes. What will matter is what’s underneath: Context. If you’ve ever wondered why GenAI sometimes gives great answers and other times sounds like it’s hallucinating or winging it — this is why. Context engineering is the discipline of giving GenAI the right information, in the right format, at the right time. It’s how we shape the environment the AI operates in — and in a knowledge industry like law, that environment is everything. When we provide proper context — the facts of a case, the jurisdiction, the client history, the structure of a document — we get far better answers. When we don’t? We get fluff. Or worse — confident nonsense. This isn’t theoretical. It’s playing out right now in every GenAI tool lawyers use — from Harvey to Newcode.ai, from Legora to Microsoft 365 Copilot. Most of us have been so focused on writing better prompts that we’ve missed what actually powers reliable legal GenAI: 🧠 Organizing our documents so they can be referenced as context 🧠 Structuring notes and transcripts so AI can find key insights 🧠 Using summaries and metadata so systems know what matters 🧠 Cleaning our data and curating knowledge so responses are grounded in our facts In my latest article, I break down what context engineering is (in plain English), how it works in legal use cases, and what you can do today — whether you're a lawyer, legal tech vendor, or innovation leader. We explore real-world examples, discuss how today’s tools are already leveraging context behind the scenes, and offer a roadmap for law firms and corporate legal teams to start engineering their context now — to get better results and reduce risk. 🚨 If you’ve tried GenAI and thought “this isn’t useful,” chances are the model wasn’t the problem. The context was. Have you seen context make or break your AI experience? Are you preparing your data to be AI-ready? I’d love to hear how your team is thinking about this. #legaltech #artificialintelligence #aiforsmartpeople #innovation

As LLMs power more sophisticated systems, we need to think beyond prompting. Here are the 3 core strategies every AI builder should understand: 🔵 Fine-Tuning You’re not just training a model — you’re permanently altering its weights to learn domain-specific behaviors. It’s ideal when: • You have high-quality labeled data • Your use case is stable and high-volume • You need long-term memory baked into the model 🟣 Prompt Engineering Often underestimated, but incredibly powerful. It’s not about clever phrasing — it’s about mapping cognition into structure. You’re reverse-engineering the model’s “thought process” to: • Maximize signal-to-noise • Minimize ambiguity • Inject examples (few-shot) that shape response behavior 🔴 Context Engineering This is the game-changer for dynamic, multi-turn, and agentic systems. Instead of changing the model, you change what it sees. It relies on: • Chunking, embeddings, and retrieval (RAG) • Injecting relevant context at runtime • Building systems that can “remember,” “reason,” and “adapt” without retraining If you’re building production-grade GenAI systems, context engineering is fast becoming non-optional. Prompting gives you precision. Fine-tuning gives you permanence. Context engineering gives you scalability. Which one are you using today?

Prompting tells AI what to do. But Context Engineering tells it what to think about. Therefore, AI systems can interpret, retain, and apply relevant information dynamically, leading to more accurate and personalized outputs. You’ve probably started hearing this term floating around a lot lately, but haven’t had the time to look deep into it. This quick guide can help shed some light. 🔸What Is Context Engineering? It’s the art of structuring everything an AI needs not just prompts, but memory, tools, system instructions, and more to generate intelligent responses across sessions. 🔸How It Works You give input, and the system layers on context like past interactions, metadata, and external tools before packaging it into a single prompt. The result? Smarter, more useful outputs. 🔸Key Components From system instructions and session memory to RAG pipelines and long-term memory, context engineering pulls in all these parts to guide LLM behavior more precisely. 🔸Why It’s Better Than Prompting Alone Prompt engineering is just about crafting the right words. Context engineering is about building the full ecosystem, including memory, tool use, reasoning, reusability, and seamless UX. 🔸Tools Making It Possible LangChain, LlamaIndex, and CrewAI handle multi-step reasoning. Vector DBs and MCP enable structured data flow. ReAct and Function Calling APIs activate tools inside context. 🔸Why It Matters Now Context engineering is what makes AI agents reliable, adaptive, and capable of deep reasoning. It’s the next leap after prompts, welcome to the intelligence revolution. 🔹🔹Structuring and managing context effectively through memory, retrieval, and system instructions allows AI agents to perform complex, multi-turn tasks with coherence and continuity. Hope this helps clarify a few things on your end. Feel free to share, and follow for more deep dives into RAG, agent frameworks, and AI workflows. #genai #aiagents #artificialintelligence

"Context Engineering: Bringing Engineering Discipline to AI Prompts" My free new deep-dive: https://lnkd.in/gJv8Wtg6 ✍ "Context engineering" means providing an AI (like an LLM) with all the information and tools it needs to successfully complete a task – not just a cleverly worded prompt. It’s the evolution of prompt engineering, reflecting a broader, more system-level approach. For too long, we've focused on the "magic" of prompt engineering – crafting clever phrases to get better AI outputs. But as AI applications become more sophisticated, it's clear we need a more robust, systematic approach. That's what context engineering is all about. In the article, I explore: - Why "prompt engineering" often falls short for real-world developer workflows and applications - Practical tips for feeding high-quality context to LLMs (code, error logs, design docs, examples, and more). - The essential role of context engineering in effectively - Strategies to manage "context rot" and maintain performance over long interactions. This shift in mindset is critical for anyone using AI. When an LLM agent misbehaves, usually the appropriate context, instructions and tools have not been communicated to the model. Garbage in, garbage out. Conversely, if you do supply all the relevant info and clear guidance, the model’s performance improves dramatically. #ai #programming #softwareengineering

Prompt engineering is out. Context engineering is in. (kind of) Prompt engineering is still important. But it's becoming less critical and it's no longer the hot thing. The hot thing now is context engineering. So what is context engineering about? Its about giving AI the right information in the right time to make the right decision (or prediction). AI models now have massive context windows. You can fit almost a million words in a single request. The models have gotten better too. But an AI can only be as good as the information it has access to. Giving very clear instructions and context to an AI is extremely important. If you ask AI to generate monthly report it can do it. But if you give it access to samples of previous reports (that are actually good). And bios of the audience. And a template to follow. Etc. It will be much better. You might think, "I've been doing that the whole time! I thought thats what prompt engineering was!" Well it is, but it extends a bit further as tool use becomes more important. I spend most of my days working on context engineering for the Hatz AI platform. When we tell an AI model how it can query a user's calendar in Outlook, we give it clear instructions on exactly what it can do, what it'll get back, and what format it needs to come in. When you upload a file that's too big for the context window, we pull back the relevant information and provide context around that information. When we have an AI making a decision inside our platform, we're giving that decision engine proper context. (like the last few chat messages or what's being asked or what the implications of a decision might be) It might be semantics between prompt engineering and context engineering. But the difference is it's less about prompting and more about getting the right information into the prompt. For some of us though, it's kind of been that way the whole time 👨🍳

The AI skills gap isn't about prompting anymore that's dead ☠️ —it's about context engineering. Two years ago, everyone was talking about "prompt engineering" as the hot new skill. Companies were hiring "prompt engineers" for six-figure salaries. The hype was real. But here's what changed: almost everyone became a prompt engineer. I did a post yesterday on just how easy this is. Writing clever prompts became table stakes. Here's where your focus should be → Context engineering. Let's break down the difference 👇 → Prompt engineering = crafting the right question for a single interaction → Context engineering = architecting the AI's entire information environment Think of it this way: If prompt engineering is writing a brilliant instruction, context engineering is deciding what happens before and after that instruction—what's remembered, what's pulled from tools or memory, and how the whole conversation is framed. This is why I feel n8n has started pulling people away from Clay because you have the ability to give an Agent more Context. This is key because: ✅ Memory & Continuity: Real applications need AI that remembers previous interactions, not blank-slate responses ✅ Accuracy: Feeding relevant documents and data reduces hallucinations dramatically ✅ Scale: A clever prompt might work in demos but fail with diverse users—context engineering builds robust systems ✅ Competitive Advantage: Anyone can tweak prompts, but architecting intelligent information flow? That's the real skill. Here's the thing, we've moved from optimizing sentences to optimizing knowledge. Anyone can build an Agent with a Prompt that just talks but most will fail at context engineering, if you can master it your Agent will truly understand you.