Turn Your AI-Generated Prototype into Production Software

What AI builders get right

It is worth being precise about this, because the useful conclusion is not that the tools are bad. They compress the slowest part of early product work — turning a vague idea into something a person can click — from weeks into a day.

The interface they produce is usually genuinely good, and the fact that it exists at all means you have already learned things about your product that a specification would never have told you. None of that has to be thrown away.

Where they consistently fall short

The gaps are systematic rather than random, because they are all in the parts that have no visible output.

• Access rules: who can see which records is frequently enforced in the browser, where anyone can bypass it.
• Money: payments work once in a happy path, and fail silently on refunds, retries and duplicate events.
• Data structure: fine at ten records, unusable at a hundred thousand, because nothing is indexed.
• Failure handling: when something goes wrong, nothing is logged and nobody is told.
• Secrets: API keys embedded where the browser can read them.

The security problem is the urgent one

Of everything on that list, access rules deserve immediate attention, because the failure mode is not an outage — it is customers reading each other's data without anyone noticing.

If your prototype has real users and real records, an access-rule audit is worth doing this week, ahead of any feature work. It is typically a day or two of somebody's time.

You do not need to start again

These tools emit ordinary code in ordinary frameworks — React, Next.js, Tailwind, and a standard database behind them. An experienced engineer can export the repository, work in it normally, and harden it piece by piece.

The sensible order is: secure the access rules, make payments reliable, fix the data structure, add monitoring, then resume building features. Each step is independently valuable, so the work can stop and restart around your budget.