Real-Time Design Feedback Systems

💡One of the most fun shifts in my workflow lately: using Figma Make not just for rapid prototypes, but for design feedback loops. Instead of waiting until design review, we now ask Make: ✨ “What feels off about this layout?” ✨ “Does the copy flow match the intent?” ✨ “Where might a user struggle with accessibility?” It gives surprisingly actionable feedback — everything from hierarchy tweaks to color-contrast warnings. Not perfect, but often enough to spark better conversations before we pull in our design leads. Shoutout to my colleague Adam Batth 👏 who showed me how he’s been using this. It’s become a lightweight “design critique buddy” for the team and I — accelerating iteration and helping us get to stronger first drafts. As a CPO, this is exactly the kind of AI assist I want my teams leaning on: not replacing judgment, but elevating the baseline so human creativity can shine where it matters most. 👉 Anyone else experimenting with AI in their design review process? Would love to compare notes. #ProductAI

Your design system documentation is always outdated. 🫠 RAG (Retrieval-Augmented Generation) fixes that. 👇 RAG connects AI to your real-time knowledge, giving you context-aware responses instead of generic ones. Here's how it works: 🔍 Retrieval: AI searches your documents, databases, or knowledge sources  ↪️ Augmentation: Combines that fresh info with the user's question 🎯 Generation:  Produces a response based on both the AI’s training and your current data Why should you care? ✅ Always up-to-date AI pulls from your latest docs ✅ Context-aware answers Knows your specific processes and terminology ✅ Cost-effective Avoid frequent model retraining. ✅ Domain-specific Uses your organization’s actual knowledge. The problem RAG solves: ❌ Generic AI responses ❌ Outdated information ❌ Expensive retraining cycles  ❌ Lack of company-specific context RAG + Design Systems = Magic ✨ 🧩 Helpful documentation: RAG can pull from your design system docs, component libraries, and design (you feed it the info) 🧩 Design decision support: Ask, "What spacing should I use for this layout?" and get specific answers based on your actual usage/guidelines 🧩 Faster onboarding: New team members get helpful, instant answers. 🧩 Component relationship mapping: RAG understands how your components work together and suggests the right patterns. Start small, test with users, then scale. 🙌 #AI #productdesign #designsystem #RAG #documentation

Turn user needs into strong design signals with UX metrics. Design signals are measurable cues that show whether your design is working. They bridge the gap between what users need and the decisions teams have to make. They’re not analytics buried in a dashboard or research reports that arrive weeks later. They’re sharp, real-time feedback that keeps an idea alive long enough to prove it works. We use attitudinal, behavioral, and performance metrics to bring clarity to our design work, often coupling them with screens: → Attitudinal metrics help you understand why people think and feel a certain way. → Behavioral metrics show how users try to complete an activity. → Performance metrics give the strongest signals, but don’t capture the opinions people might share. As I discussed with Douglas Curtis in our Glare forum, signals aren’t just for validation. Strong signals can also reveal weak or undefined user needs, often starting as a hunch. As you explore ideas, there’s a push and pull to align the interface with user needs. This process can challenge and reshape those needs as people experience something new. We often use five core metrics in a design stack in Helio to strengthen a signal. Here’s why signals matter: → They’re clues to what might be emerging in the future. → They’re different from noise, which is just distraction or irrelevant data. → Recognizing them means paying attention and interpreting in context. → They can start out weak and be easy to miss without scanning for them. → They inform foresight, strategy, and timely decisions. We’ve spent over 50,000 hours testing and refining how to capture these signals across startups, scaleups, and enterprises moving fast without losing user understanding. 👋 DM me if your best ideas keep getting stuck or you want faster ways to surface signals in your design team. #productdesign #uxmetrics #productdiscovery #uxresearch