When AI moves from prototype to production, the biggest risk is inconsistent behavior under real user input.
A practical validation checklist:
- Define deterministic acceptance checks for critical flows.
- Add safety and policy checks before model outputs are shown.
- Validate latency and fallback behavior under load.
- Track regressions with weekly benchmark snapshots.
This kind of checklist keeps experiments aligned with production reliability.