The fastest way to lose trust in an AI feature is to ship a demo and call it a product. Real AI features earn their place by solving a narrow problem reliably, then expanding from there.

Start with the job, not the model

Pick a concrete task with a measurable outcome — summarize this, classify that, draft the other. A sharp problem makes evaluation possible and keeps scope honest.

Build an evaluation harness early

Before you tune prompts, decide how you'll know the feature is good. A small, representative test set turns "feels better" into a number you can move.

  • Define success criteria with real examples
  • Track quality across changes, not just vibes
  • Keep a human in the loop where the cost of error is high

Design for graceful failure

Models are confident even when they're wrong. Show sources, allow easy correction, and fail softly so a bad answer is a minor annoyance rather than a broken experience.

Ship small, measure, expand

Launch to a slice of users, watch real usage, and grow the feature where it's clearly earning its keep.