All writing
25 checks before taking an AI-built project to production
AI makes a product look finished faster. That makes verification more important, not less. Production readiness is about known behavior under failure, not only a working happy path.
AI coding can turn an idea into a convincing product surface very quickly. Before release I broaden the definition of done beyond a successful build.
The 25 checks
- Correct repository, branch and commit.
- Clean working tree.
- Typecheck, lint and production build.
- Unnecessary dependencies removed.
- No private secret in the client bundle.
- Session persistence tested.
- Logout semantics verified.
- Authorization enforced at the data/server layer.
- Data ownership explicit.
- Database constraints present.
- Critical mutations idempotent.
- Deletion semantics defined.
- Duplicate fetching measured.
- Background polling controlled.
- Cache policy defined.
- Timeout and retry bounded.
- Loading behavior designed.
- Empty states designed.
- Error recovery clear.
- Real-device mobile behavior tested.
- 404 and route fallbacks correct.
- Canonical and social metadata complete.
- Sitemap and robots accurate.
- Production failures observable.
- Rollback possible.
const productionReady = codeVerified && authVerified && dataVerified && networkMeasured && failureStatesDesigned && monitoringVisible && rollbackPossible;From Prototype to Production
Turning a working MVP into a dependable product: production checks, modular refactoring, data migration, audits, rewrite decisions and release gates.