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This Use Case has not been finalized.
Define·Strategic Framing·Augmentation·Developing·DEF-102
Research-to-Requirements
Value hypothesis
Compresses requirements drafting from days to hours by generating structured, reviewable documentation from discovery inputs, reducing blank-page paralysis and increasing the proportion of evidence that makes it into the specification.
Velocity · Quality
The practitioner provides AI with discovery outputs — interview transcripts, research synthesis documents, opportunity maps, meeting notes, rough feature descriptions — and prompts it to produce a structured first draft of requirements documentation: a PRD, a set of user stories with acceptance criteria, or a functional specification. AI transforms unstructured or semi-structured discovery evidence into formatted requirements artifacts, proposing scope boundaries, user stories, edge cases, and success metrics. The practitioner reviews the draft for strategic fit, domain accuracy, completeness relative to the actual problem, and alignment with organisational conventions, then iterates with AI to reshape the document. The intended outcome is a reviewable requirements draft that captures the breadth of discovery evidence and exposes gaps early, produced at a fraction of the manual drafting time. Subsumes PRD generation and requirements traceability as capabilities within this UC — these are not standalone use cases.
Risks in application
Pseudoproductivity
AI-generated requirements documents are dangerously polished — structurally complete, well-formatted, and internally consistent — which makes it easy to mistake a plausible draft for a vetted specification. The practitioner experience report (SR-089) explicitly flags this as the primary trap: the first draft looked so complete that it felt done, masking the absence of original strategic thinking.
Shallow Solutions
AI-generated requirements documents fill in detail the source material did not contain: edge cases, acceptance criteria, and error states get articulated in the document as if they had been considered, when they were extrapolated from convention.
Expertise that differentiates
Business Framing
Evaluating whether AI-drafted requirements accurately translate user needs into buildable scope — distinguishing between what users said, what they meant, and what the business can actually deliver within constraints the AI cannot see.
Research and Insight
Assessing whether requirements trace back to genuine evidence rather than AI-interpolated assumptions — particularly when the AI fills gaps in the source material with plausible but unsupported specifications.
AI Fluency that assures
Product Description
Provides AI with discovery outputs — interview transcripts, research synthesis documents, opportunity maps, meeting notes, rough feature descriptions — and prompts it to produce a structured first draft of requirements documentation: a PRD, a set of user stories with acceptance criteria, or a functional specification.
Process Discernment
Assessing whether requirements trace back to genuine evidence rather than AI-interpolated assumptions — particularly when the AI fills gaps in the source material with plausible but unsupported specifications.
Evaluating whether AI-drafted requirements accurately translate user needs into buildable scope — distinguishing between what users said, what they meant, and what the business can actually deliver within constraints the AI cannot see.
Possible Indicators
Time to first draft
Elapsed time from discovery completion to a stakeholder-reviewable requirements document
Synthesis coverage
Proportion of discovery evidence incorporated into the requirements document versus manual baseline — measuring whether AI-assisted drafting captures more of the evidence base
Sources
Author unknown (n.d.). ChatPRD — The #1 AI Platform for Product Managers. ChatPRD.
Filippov (n.d.). Role of AI in Requirements Engineering. Xray.
Author unknown (n.d.). AI PRD Generator: Create Product Requirements Fast. Miro.
Author unknown (n.d.). AI PRD Generator: Write Better Product Requirements with AI. Figma.
Sikder (n.d.). We Used AI Tools to Write Our PRD — Here Are the Results. Medium.
Gantayat (n.d.). Generate User Stories Using AI | 21 AI Prompts + 15 Tips. Agilemania.
Böckeler (n.d.). Using AI for Requirements Analysis: A Case Study. Thoughtworks.