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Building & Product

My AI feature is live. How do I monitor it and make it better with real usage?

Log every AI call with its prompt, response, cost, latency, and user feedback; observability tools like Langfuse or LangSmith give you this in an afternoon and turn debugging from guesswork into search. Watch AI-specific metrics, not just usage: task completion rate, retention of AI-feature users versus everyone else, and thumbs-down clusters that reveal failure patterns. Feed real production failures back into your eval set so every incident permanently raises your quality bar.

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Retention

More in AI in Your Product

Should my startup add AI features, and which ones actually matter to users? 20 → How do founders ship their first AI feature (APIs, wrappers, RAG in plain terms)? 19 → How much does it cost to run AI features, and how do I keep costs sane? 19 → Which AI model should I build on (GPT, Claude, Gemini, open-source), and how do I avoid locking myself in? 17 → How do I test whether my AI feature actually works before launch (evals, in plain terms)? 18 → How do I stop my AI feature from making things up in front of customers? 17 → Prompt engineering, RAG, or fine-tuning: which one does my product actually need? 18 → How do I design AI features people trust (chat vs buttons, streaming, showing the work)? 18 → What is my moat if I'm building on someone else's model? 19 → Should I put an AI agent in my product, or is a simple workflow enough? 18 → Is it safe to send customer data to AI APIs, and what about privacy laws like India's DPDP Act? 18 → How should I price AI features: per seat, per use, or per outcome? 17 →

Also in Starting Up

The same ground, over in Build the product, our Starting Up track.

What actually counts as an MVP, and what's the smallest one I can get away with? 20 → Which no-code tool should I use to build my first product? 20 →
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