AI built the prototype.
Now the real product decisions start.

Stop prompting long enough to evaluate what exists.

Once a prototype works, it is tempting to immediately ask the model for ten more features. Instead, freeze the feature list briefly and use the product like a stranger would.

• What is the main outcome?
• Can a new user reach it without your explanation?
• Which parts are real versus mocked or hard-coded?
• What data is being stored, where, and under whose account?
• Which external services does the product depend on?
• What happens when those services fail?

Validate the product before polishing the implementation.

If nobody wants the core outcome, cleaning the architecture does not rescue the idea. Put the prototype in front of the right people early enough that you can still change what the product is.

Then inspect the risky foundations.

Authentication, permissions, billing, secrets, user data, uploads, and destructive actions deserve more attention than a slightly ugly component. The level of specialist review you need should match the consequences of failure.

Decide what kind of next step you actually need.

You might need user feedback, a UX cleanup, a code review, better QA, a narrower scope, or a specialist engineer. Those are different jobs. Do not turn “the prototype feels unfinished” into “rewrite everything” by default.