What to check before shipping an AI-generated app

Before reading every generated file, use the app like a real user. Sign up, create the first object, hit the empty state, make a mistake, and try the success path again on mobile.

This catches the problems that code review can miss: unclear labels, broken hierarchy, missing loading states, and flows that technically work but do not feel trustworthy.

Generated code is still code. The useful question is not whether AI wrote it; the useful question is whether the structure is readable enough for a human to own next week.

Look for the places where product decisions become infrastructure decisions. That is where small mistakes turn into expensive cleanup.

• Check route boundaries, shared components, and duplicated logic before adding more screens.
• Confirm database tables, row ownership, and auth rules match the product model.
• Move secrets into environment variables and keep example values out of the repo.
• Read error states and empty states as carefully as the happy path.

A preview is not the finish line. Before shipping, confirm the app can build from a clean checkout, read the required environment variables, and recover from a failed request without exposing private details.

The deployment path should be boring: build, preview, publish, roll back. If any of those steps depend on hidden state inside the builder, the app is not ready to belong to the team.

The strongest AI builder does not make the user dependent on it forever. It leaves behind a readable repo, a clear stack, and a project another engineer can inspect without asking the original generation tool for permission.

That is the real test before launch: not whether the demo looked fast, but whether the product can keep moving after the first generated version.