Briefly PRD AI
Live portfolio applicationTransforms rough product concepts into decision-ready PRDs with guided intake, user stories, acceptance criteria, success metrics, and Markdown export.
Problem & approach
Problem
Early product concepts need a structured requirements draft before cross-functional review.
Approach
Guide contextual intake, generate an editable draft, and export Markdown.
Evidence
Review the live workflow and the generation route; assess drafts against actual customer evidence.
Product decision review
Who it serves
Product managers turning an early concept into an editable requirements draft for discussion with engineering and design.
The decision this design supports
Use guided intake to make the problem, audience, constraints, and intended outcome explicit before generating a PRD. Keep the document editable so a model response remains the beginning of review, not a substitute for product judgment.
Implemented workflow
- Enter product context and constraints through the guided form.
- Generate a structured draft through the server-side model endpoint.
- Review user stories, acceptance criteria, assumptions, and proposed metrics.
- Edit the draft, keep it in browser storage, and export Markdown for the next planning discussion.
Design tradeoff
Local draft storage avoids needing an account and a document database, but it ties drafts to the browser. Markdown is portable and easy to review, while collaborative editing and approval workflows would require a different persistence model.
What to measure
Useful evaluation criteria include time to a reviewable draft, missing acceptance criteria, unsupported assumptions, and reviewer edits. No numerical improvement is claimed without a recorded study. A completeness score is a checklist signal, not proof of requirement quality.
Current scope
Generation needs configured model credentials and usage controls. The live UI implements Markdown export. Drafts must be checked against actual customer evidence and technical constraints.
Next evaluation
Evaluate drafts against a fixed rubric with blind human review, and add explicit labels for assumptions and unknown metric baselines.
Inspect the implementation
Release history
GitHub releases are tagged versions. Implementation code and commits can exist without a published release.