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Building & Product

How do I stop my AI feature from making things up in front of customers?

Models hallucinate because they are trained to always answer; your job is to constrain them, not to hope. The biggest wins in order: ground answers in your own retrieved data with citations (RAG), force structured outputs so responses are validated before display, let the model say "I don't know", and add guardrails plus human review on high-stakes actions. Air Canada and Cursor both learned publicly that you own whatever your bot says, so treat reliability as a launch requirement, not a polish item.

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17 resources.

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 → 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 → My AI feature is live. How do I monitor it and make it better with real usage? 18 →

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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