AI-Generated PRDs: How to Review and Improve Them

An AI-generated PRD that ships to engineering unreviewed is worse than no PRD at all. It gives the appearance of rigour without the substance — and engineers discover the gaps at the worst possible moment.

But reviewing AI output is a different skill from writing a PRD from scratch. You're not filling a blank page; you're fact-checking a confident-sounding document for errors it doesn't know it's making. This guide gives you a systematic approach.

The Four Failure Modes to Hunt For

Before running any checklist, it helps to understand what AI gets wrong in PRDs specifically. There are four recurring failure patterns.

Hallucinated specifics — AI invents precise-sounding details: response time thresholds, character limits, user counts, API constraints, competitor feature parity claims. These are plausible but fabricated. They embed false assumptions into requirements that engineering will implement faithfully.

Generic requirements — Requirements that sound correct but apply to any product in the category rather than yours specifically. "Users should be able to search and filter results efficiently" describes search for every B2B SaaS tool ever built. It's not a requirement — it's a category description.

Missing judgment calls — AI fills gaps with the most common answer, not the right answer for your context. Prioritisation, scope decisions, and trade-off choices get resolved by statistical average rather than your product strategy.

Absent constraints — AI doesn't know what you can't build. Technical debt, team capacity, platform limitations, regulatory constraints — none of these appear unless you explicitly provided them as input. The PRD will specify things engineering can't deliver without signalling that this is a problem.

The Review Checklist

Work through this checklist section by section on every AI-generated PRD before sharing it with engineering or stakeholders.

Background and problem statement

• [ ] Does the problem statement match what your research actually shows? Read it against your interview notes, not against your intuition.
• [ ] Are there any claims about user behaviour or frequency that you can't trace to a source? Mark them as [VERIFY] or replace with what you actually know.
• [ ] Does the framing accurately describe your specific user segment, or is it describing a generic version of your user?
• [ ] Are there any invented statistics (e.g. "68% of users report...") that don't come from your data? Delete them.

User stories

• [ ] For each user story, ask: did your research show this need, or is this a plausible guess? Keep stories grounded in evidence; move speculative ones to a "future consideration" appendix or cut them.
• [ ] Are the user types accurate? AI often invents user roles that sound reasonable but don't reflect your actual user segments.
• [ ] Is the "so that" clause specific to your product's value proposition, or is it a generic benefit statement?
• [ ] Are there user stories for user types you're not building for in this iteration? Remove them or explicitly mark them as out of scope.

Acceptance criteria

• [ ] Are there any acceptance criteria that specify implementation details? (e.g. "the database query should complete in under 50ms") — these belong in engineering design docs, not PRDs.
• [ ] Are there any invented thresholds? (e.g. "support up to 10,000 concurrent users") — check these against your actual scale requirements or mark as [TBD].
• [ ] Does each criterion have a clear pass/fail state? Vague criteria ("the interface should feel responsive") can't be tested.
• [ ] Are error states, empty states, and loading states covered? AI often omits these even when the happy path is comprehensive.
• [ ] Are permission or role-based variants covered for your user tiers?

Success metrics

This is the section you should rewrite entirely, not just review.

AI-generated success metrics default to generic product health indicators (DAU, conversion rate, NPS). Your actual metrics need to reflect:

• Your current measurement framework
• Your team's agreed OKRs for this quarter
• Baseline data you already have
• A specific timeline for measurement

Write this section yourself. Delete what AI generated and replace it.

Open questions

AI generates plausible-sounding open questions — but they're often not your actual open questions. They're the open questions someone building a generic version of your feature would have.

Review each open question:

• Is this actually unresolved for your team?
• Is this something you already know the answer to?

Then add the open questions AI missed — the ones only someone familiar with your product would know to ask.

Out of scope

AI rarely writes a strong "not doing" section. This section is as important as the requirements — it prevents scope creep and sets engineering expectations.

Add explicit out-of-scope items covering:

• Features that came up in research but aren't being addressed in this iteration
• Edge cases you're consciously deferring
• Integrations or platforms you're not supporting yet

A Faster Editing Workflow

Don't try to review the whole document in one read. Use two passes:

Pass 1 — accuracy pass (20 minutes)

Read every factual claim and requirement with the question: "Is this true for my product and my users?" Mark anything unverified with [VERIFY] and anything invented with [INVENTED]. Don't rewrite yet.

Pass 2 — judgment pass (25 minutes)

Rewrite or delete everything you marked. Add the missing constraints, the accurate metrics, and the open questions only you know. Write the out-of-scope section.

The edited document that emerges from these two passes will be more accurate than a fully human-written PRD produced under time pressure — because AI covered the structure and edge cases, and you supplied the accuracy and judgment.

What Good Looks Like After Review

A reviewed AI PRD should pass this final check:

• Every requirement traces to either customer evidence or an explicit business constraint
• Every number or specific threshold has a source or is marked as a placeholder
• The success metrics reflect your actual measurement framework
• The out-of-scope section explicitly covers things that came up in research and were deliberately excluded
• The open questions are the ones your team will actually need to resolve, not invented ones

If you can hand the document to an engineer and answer "why are we building this?" for every major requirement, the review was thorough enough.

AI writes PRDs faster than humans. Humans catch what AI gets wrong. The review step is where the time savings from AI generation get reinvested into quality — don't skip it.