A practical playbook for AI features that don’t embarrass you

The failure mode of most AI features isn’t that the model is bad — it’s that there’s no evaluation and no fallback. When the model is wrong, the user sees a confident wrong answer and learns to distrust the whole product.

Our playbook for production AI features:

1. Define the evaluation first. A labeled set of real inputs with expected behavior. If you can’t measure it, you can’t ship it.
2. Design the fallback. Every AI answer should have a graceful, correct-by-default path when confidence is low.
3. Log and review. Capture a sample of production inputs/outputs for periodic review — the model drifts.
4. Bound the blast radius. Keep AI assistive in high-stakes flows; never let an LLM own an irreversible action without a human.

Teams that treat the model as a fallible component — not a black box — ship AI features that users actually trust.