# The moment I stop prompting and start scoring an AI-generated MVP

> Source: <https://dev.to/vivian_chi_5018aa69d5ef43/the-moment-i-stop-prompting-and-start-scoring-an-ai-generated-mvp-46lg>
> Published: 2026-06-13 01:15:59+00:00

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](https://www.nxcode.io/) 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](https://www.nxcode.io/docs/getting-started) 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?