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

A developer using NxCode describes a five-check scoring system to validate AI-generated prototypes before committing to engineering work. The process includes verifying object lists, state transitions, edge cases, and cutting 20-30% of scope to ensure the prototype is usable, not just visually complete.

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?