Using AI prototyping to find the holes in your own requirements

A committed AI skeptic owned a 0-to-1 product and tried prototyping with AI only because the timeline forced it. Their conclusion was narrow and honest: they'd never ship AI-written code, but it does one thing genuinely well — it helps you write strong requirements. They prototyped a CSV-upload-and-map tool with a tangle of conditional validations, knew the generated code was trash, and used it anyway to see what their requirements had missed. It made the gaps obvious, and the next PRD was far better for it.

This is one of the most useful and least hyped uses of AI in product work, precisely because it's the opposite of "AI writes my spec." The model isn't producing the artifact you keep. It's producing a disposable probe that reveals what your real artifact is missing.

Why a prototype exposes requirement gaps

When you write requirements, you reason in the abstract, and abstraction hides holes. "Let the user map fields and correct errors" sounds complete on the page. The moment something tries to build it, every unspecified case becomes a forced decision: what happens when two columns map to the same field, when the file is empty, when an error is corrected to another error? The prototype can't proceed without an answer, so it either asks or guesses wrong — and either way, the hole in your spec is now visible. You couldn't see it on the page because the page never forced the question.

How to use it well

• Treat the code as disposable from the start. You're not building; you're probing. The instant you start trying to keep the code, you've switched goals and you'll get attached to something that should be thrown away.
• Watch where it diverges from your intent. The places the prototype did something you didn't mean are the places your requirements were ambiguous. That divergence is the output you wanted.
• Feed the gaps back into the spec, not the prototype. The win is the improved PRD, with the holes closed. The prototype's job ends the moment it's shown you the holes.

The skeptic was right to stay skeptical of shipping the code and right to find the one real win. AI prototyping isn't a way to skip the spec. It's a way to stress-test the spec by making the abstract concrete — and concrete is where the gaps finally show.