AI feature deployment checklist
Shipping an AI feature is not like shipping ordinary code. The output is probabilistic, the failure modes are weird, and the worst bugs only show up against real, messy, adversarial input. This checklist is a 30-point pre-flight built from the things that actually bite teams in production: untested edge cases, missing fallbacks, leaked PII, no monitoring, no disclosure to users, and no way to roll back. Work through it, get the readiness bar green, then ship.
How it works
You describe the feature — its type, whether it touches user data, and whether it is production-bound. The tool filters the master list down to the checks that apply and groups them into categories: prompt and output testing, edge cases and fallbacks, data and privacy, monitoring and observability, user experience and disclosure, and rollback and safety. Each item is weighted, with the critical ones flagged. As you tick items off, a live readiness score tells you how close you are, and remaining work is grouped so you can see what is left. Your progress is saved locally so you can come back to it, and you can copy the whole checklist with its state into a launch ticket or PR.
Tips and notes
- Green the critical items first. Fallback logic, PII handling, and a tested rollback are non-negotiable; cosmetic checks can wait.
- Test with hostile input. The happy path always works in the demo. Paste in the weird, empty, huge, and malicious inputs before you ship.
- Disclose AI use. Users — and increasingly regulators — expect to know when they are talking to or being judged by a model.
- Confirm the kill switch. If you cannot disable the feature in seconds, you are not ready to enable it.