Create a launch checklist for OpenAI-powered apps

Real workflow example

A team is ready to launch an AI assistant that drafts account follow-ups. The prompt works in internal demos, but launch readiness requires more than a good answer. The product needs permission checks, validation, fallback behavior, cost tracking, support notes, and a way to inspect bad outputs.

The launch checklist turns the feature from a prompt into an operated system. It gives engineering, product, and support a shared definition of what is safe to release.

Implementation approach

Run representative examples through the exact production path: UI, server route, model, tools, schema validation, database write, review screen, and final action. Do not validate the prompt in isolation.

Check failure behavior deliberately. Trigger rate limits, validation failures, missing permissions, empty files, malformed output, and tool timeouts. The user should always land in a clear state.

Prepare support and rollout. Small cohorts, usage dashboards, cost limits, and support notes matter more than a dramatic full release.

Code or config snippet

## AI launch checklist

- [ ] Representative eval set passes on production model and schema.
- [ ] API keys are server-side only.
- [ ] Auth and organization access are checked before every scoped read or write.
- [ ] Model output is validated before database writes or external sends.
- [ ] Tool failures create retry, review, or blocked states.
- [ ] Usage, latency, errors, review decisions, and cost are tracked.
- [ ] Support has recovery notes for expected failures.
- [ ] Rollout starts with a limited user segment.

Mistakes to avoid

• Launching because the best demo example works.
• Testing the model call but not the full workflow.
• Forgetting support copy for expected failures.
• Shipping without cost visibility.
• Treating human review as optional when downstream actions are risky.

Ready checklist

• Production examples pass from UI to final action.
• Auth, permissions, and ownership checks are verified.
• Structured output validation blocks bad data.
• Fallback behavior is documented and visible.
• Usage and cost dashboards exist.
• Rollout starts with a small cohort and rollback path.