I do not trust an AI-generated MVP when it first looks good.
I trust it only after I can score it.
That is the point where I stop writing bigger prompts and start running a small review loop against the output. Lately I have been doing that with NxCode because it gets me from a rough product idea to a reviewable app structure quickly enough to make the scoring pass worth doing.
I use 5 checks before I let a prototype become engineering work.
If I need three paragraphs to explain the flow, the prototype is still too vague. Example:
That sentence becomes the test for everything else.
Before reviewing UI details, I write the smallest possible object list:
If the screens cannot clearly support those objects, I know the app is still theater. This is the check that catches the most fake completeness.
I look for:
If the prototype hides those transitions, I mark it incomplete. I always test one "ugly" case early:
That tells me whether I am looking at a clean story or a usable workflow.
This is the most important score in the loop.
If I cannot remove at least 20-30% of the requested scope after the first prototype, I probably generated too much surface area. Typical cuts:
The value is not "AI built the app for me."
The value is:
That is a much better use of an AI app builder than asking it to impress me with speed alone.
If you are trying the same kind of workflow, the NxCode docs are a good place to start. That human review is still the part that keeps the MVP honest.
What is the first score you apply before you trust an AI-generated prototype?