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BusinessPRDsintermediate

Zero-to-One Product Requirement Document (PRD) Author

Draft comprehensive, engineering-ready PRDs with user stories, acceptance criteria, and edge cases.

Compatibility & Specs

Compatible AI Models
ClaudeChatGPT
Last UpdatedMar 25, 2026
Customizable Variables4 parameters

How to Use This Prompt

Follow this 3-step workflow to extract high-signal responses from any compatible AI model.

01

1. Tailor the Parameters

Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.

02

2. Send to AI Model

Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.

03

3. Review and Iterate

Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.

Prompt Variables & Parameters

Reference breakdown of every dynamic variable embedded in this prompt template.

PlaceholderParameter NameTypeStatusDescription & Guidance
[feature_idea]Feature / Product IdeatextRequiredWhat are you buildingDefault: Automated AI Code Review Assistant for Pull Requests in GitHub
[target_persona]Target User PersonatextRequiredWho is the primary userDefault: Full-Stack Software Engineers and Engineering Managers
[success_metric]Success MetrictextRequiredPrimary quantitative KPIDefault: Reduce PR review cycle time by 35% without increasing post-release defect rate
[technical_constraints]Technical ConstraintstextareaOptionalSecurity, latency, or API constraintsDefault: Must run in <30 seconds per PR; zero retention of customer proprietary code; supports GitHub Webhooks.

Example Execution & Benchmark Output

Sample input arguments and the verified AI response demonstrating expected quality and formatting.

Sample VariablesInputs
[feature_idea]
Automated AI Code Review Assistant for GitHub PRs
[target_persona]
Full-Stack Software Engineers
[success_metric]
Reduce PR review cycle time by 35%
[technical_constraints]
Must run in <30s; zero retention of customer proprietary code
Expected AI ResponseVerified Result
### Product Requirement Document (PRD) **Feature**: GitHub AI Code Review Assistant **Status**: Ready for Engineering Review #### 1. Functional Requirements (P0 - MVP) - **FR-01 (Webhook Ingestion)**: System must securely ingest `pull_request.opened` and `pull_request.synchronize` events within 500ms. - **FR-02 (Diff Parsing)**: Filter out auto-generated files (e.g. lockfiles, minified bundles, SVGs) from evaluation. - **FR-03 (Inline Commenting)**: Post comments only on lines with high-confidence issues (>90% confidence), limiting to max 5 comments per PR to prevent comment fatigue.

Best Use Cases

Scenarios and roles where this prompt produces maximum leverage.

Product managers transitioning from feature ideas to engineering sprints
Founders defining scope before hiring freelance contractors
Tech leads standardizing specification documents across cross-functional squads

Tips for Best Results

Techniques to elevate response fidelity

  • •Explicitly define your Ideal Customer Profile (ICP), their acute pain points, and current legacy alternatives.
  • •Provide strict negative constraints: list corporate clichés, overused jargon, or vague promises the model must avoid.
  • •Ask the model to critique its own copy from the perspective of a cynical, time-pressed prospect before finalizing.

Common Mistakes to Avoid

Frequent failure modes and anti-patterns

  • •Failing to define a singular, clear Call-to-Action (CTA), resulting in unfocused and diluted messaging.
  • •Accepting high-level marketing buzzwords that sound impressive but say nothing concrete to users.
  • •Not fact-checking competitive claims, legal guarantees, or pricing specifications generated by the model.

Part of Curated Collections

This prompt is sequenced as part of these goal-oriented workflows

View all collections

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