A fluent document can conceal missing decisions
An AI-generated product requirements document may look complete while inventing user needs, technical assumptions, or acceptance criteria. The polish makes those gaps easy to overlook. Begin with a decision brief and source material, then ask the assistant to make uncertainty visible rather than filling every heading.
Supply the inputs that matter
Provide the problem, audience, evidence, desired outcome, constraints, decisions already made, and known open questions. Separate required behavior from candidate ideas. Include relevant design and engineering context with permission. A generic prompt cannot reconstruct a team’s unstated assumptions reliably.
Ask for a bounded draft
Request a problem statement, scope, non-goals, key flows, edge cases, and open decisions. Tell the model to identify missing information. Link claims to source IDs where possible. Do not ask it to turn a rough idea into a finalized implementation contract without review.
Review with the people doing the work
Design can challenge the experience and missing states. Engineering can assess feasibility and dependencies. Data partners can inspect measurement. Commercial or operational teams can clarify real constraints. Their input is part of making the requirements useful, not a formality after the document is written.
Make acceptance criteria observable
Replace vague criteria such as “easy to use” with behavior the team can inspect. Include failure, recovery, accessibility, and relevant performance expectations. Keep prototype criteria distinct from production readiness. Ask the AI to find ambiguity, then decide how to resolve it.
Keep the document connected to decisions
Record meaningful changes and why they occurred. Remove outdated assumptions. A useful PRD helps collaborators understand what they are building and what remains open. Document length is not the measure of quality; shared understanding and better execution are.