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Checklist before you scale AI features

Launch Details

Launching LLMs into production requires careful planning, rigorous testing, and robust monitoring. This checklist covers essential steps, from evaluating vendor SLAs to establishing fallback mechanisms. Make sure your team has a clear strategy for handling edge cases, monitoring budget limits, and preventing data leakage before you scale your AI features to thousands of users.

Before you spend more on AI, make sure you can tell whether it is working—and stop it quickly if it is not.

1. Test cases you keep — a fixed set of questions and the answers you expect, rerun after every change.
2. Logs you can read — record what the AI did, how long it took, and what data it used.
3. Human spot-checks — someone reviews a sample of real outputs every week.
4. Cost limits — budgets per customer or feature so bills do not surprise you.
5. A way to turn it off — switches to roll back to a previous version without redeploying everything.

We build these steps into AI projects from the start—not as a cleanup task at the end.