A product requirements document should help a team make and execute decisions. AI can improve its structure, identify gaps, and draft examples. It cannot supply customer evidence you have not collected or resolve a tradeoff the team has not made.
Begin with a decision brief, not an empty prompt asking for a comprehensive PRD.
Supply the decisions before requesting the document
Write the customer, the task they are trying to complete, the evidence that something is wrong, and the outcome you want to improve. Add the current experience, constraints, rejected alternatives, and unresolved questions.
For a hypothetical onboarding improvement, the problem might be that new administrators cannot tell which permissions an invitation grants. The proposed outcome is successful, correctly permissioned invitations. “Build an AI onboarding assistant” is a potential solution, not the problem statement.
Ask the model to distinguish agreed requirements from proposals. Missing decisions should remain visible rather than being converted into confident prose.
Draft a small, inspectable structure
Use these sections: problem and evidence; intended users; desired outcome; scope and exclusions; user journey; functional requirements; failure and recovery behavior; acceptance criteria; measurement; dependencies; open decisions.
Try this prompt:
Draft a PRD from the attached decision brief. Preserve source IDs. Label each requirement Agreed, Proposed, or Needs decision. Do not add unsupported features. Include a simple alternative to the proposed solution. For each acceptance criterion, state the starting condition, user action, and observable result. List contradictions and missing decisions before the draft.
Review the missing decisions first. If the model cannot tell who can edit an invitation, resolve permissions before discussing button copy.
Make requirements testable
“We need an intuitive invitation flow” is not a testable requirement. “Before sending, the administrator can see the invitee's role and the access that role grants” is clearer.
Include failure cases. What happens when an email is invalid, the invite already exists, the network times out, or the person lacks permission? Identify which action can be retried and how duplicate invitations are prevented.
For AI behavior, avoid requirements such as “The assistant always provides an accurate answer.” Define the source material it may use, the conditions for asking a question, and the situations where it must not act. Link the PRD to an evaluation set rather than promising perfect output.
Use AI as a reviewer
After writing, ask for three reviews: a user's perspective, an engineering perspective, and a measurement perspective. These are simulated critique lenses, not substitutes for those actual collaborators.
A useful follow-up is: “Identify the smallest change that could test the central assumption. Which requirements could wait without invalidating the test?” This often reveals that the draft has become a release plan before the team has validated the problem.
Review with the people doing the work
Ask design to challenge the task flow, engineering to challenge feasibility and failure handling, and analytics to inspect the measurement plan. Record decisions in the document so readers can distinguish discussion from agreement.
The definition of done for a PRD is not that every heading has text. It is that the team understands the intended outcome, can identify what is in scope, and knows which unresolved issues could change the plan.
Try it: Start with the AI-ready PRD Template. For a difficult tradeoff, use the site's Product Decision Session.