Production Readiness Checklist: Is Your AI‑Generated App Ready to Launch?
Your app looks great in a demo. Launch day asks for more. Production readiness is the difference between “works on my laptop” and “works every day for real users.” With vibe‑coding and AI app generation, you can reach readiness quickly by focusing on the basics that matter most.
The essentials before you invite users
- Auth and onboarding work from start to finish on the live site
- Friendly errors guide people when something goes wrong
- A few integration tests protect the core paths you rely on
- Logs exist so you can see failures without guessing
- Backups or exports protect user data from accidental loss
A quick pass on stability and trust
Click through sign‑up, sign‑in, and a small edit. Try them on desktop and phone. Ask a teammate to try the same steps and see if they get stuck anywhere. If payments or admin features exist, check them with a second account and avoid testing as an administrator only.
Keep launch light and repeatable
Use preview deployments to test changes on real infrastructure before launch. Keep environment settings in one place and use the same build command everywhere. Write down how you confirm a release is healthy so the steps become routine.
After launch, listen and improve
Add a simple way for early users to report issues, then fix only what they felt first. Launch is not the finish; it is the start of learning. A steady cadence of small improvements beats a big rewrite later.
If you want a second set of eyes before launch or need help hardening an AI‑generated MVP, Spin by fryga can run a focused readiness check and help you close the last gaps.
A one‑week launch plan
Day 1–2: Finalize the core journeys, write down the success criteria, and verify environment settings.
Day 3: Add friendly errors and a basic log you can check after release.
Day 4: Run through preview builds on desktop and mobile; fix only blockers.
Day 5: Invite a small beta group and collect feedback through a short form.
Day 6–7: Fix the top three issues the beta felt most; prepare the public announcement once those are resolved.
Common last‑mile misses
- No clear way for users to report problems
- Testing only as an admin, missing what regular users see
- Forgetting to verify email sending or payment flows in production
Close these gaps and launch day becomes a calm, repeatable routine.
Founder FAQs
Do we need monitoring from day one? Yes, in a small form. Capture errors and basic performance so you can see what users experienced after release. You can add depth later.
Should we harden everything before launch? No. Harden the paths people will touch first: onboarding, the main action, and any payment or admin steps. Improve the rest as usage grows.
Which tools? Choose what matches your stack and skills. Many AI‑first teams pair a simple host (Vercel/Netlify) with a lightweight error tracker and basic analytics. The best setup is the one you will actually check.
Case study: from demo to dependable
A founder deployed an AI‑generated MVP to Vercel. Users saw blank screens when the first API call failed. They added a friendly error page, logged failures, and wrote two tiny integration tests for onboarding and save. The next release felt stable, support dropped, and investor demos became calm.