PRD Generator

Overview

The PRD Generator skill helps AI Product Managers create comprehensive, well-structured Product Requirements Documents (PRDs) that incorporate industry best practices, PM frameworks, and AI-specific considerations. It transforms high-level feature ideas into detailed specifications ready for engineering and stakeholder review.

When to Use This Skill

• Starting a new feature or product initiative
• Need to document requirements for engineering team
• Preparing for stakeholder review or approval
• Creating specification for AI/ML features
• Transitioning from discovery to delivery phase
• Documenting complex features with multiple components

PM Frameworks Applied

• Jobs-to-be-Done (JTBD): Frames problem from user's perspective
• SMART Goals: Ensures success criteria are Specific, Measurable, Achievable, Relevant, Time-bound
• RICE Prioritization: Calculates expected impact (Reach × Impact / Effort)
• User Story Format: "As a... I want... So that..." structure
• MoSCoW Method: Categorizes requirements as Must/Should/Could/Won't have
• RACI Matrix: Defines stakeholder roles and responsibilities
• AI Product Canvas: Structured approach for AI feature requirements

Inputs Required

{
  "feature_name": "string",
  "problem_statement": "string",
  "target_users": "string or array",
  "business_goals": "array of strings",
  "user_research_summary": "string (optional)",
  "competitive_landscape": "string (optional)",
  "technical_constraints": "array of strings (optional)",
  "success_metrics": "array of objects (optional)",
  "ai_ml_requirements": {
    "model_type": "string (optional)",
    "data_requirements": "string (optional)",
    "performance_targets": "object (optional)"
  }
}

Outputs Produced

A comprehensive PRD document in Markdown format containing:

1. Executive Summary - One-page overview
2. Problem Statement - JTBD-framed user problem
3. Opportunity Sizing - Market size and impact potential
4. Success Metrics - Quantifiable KPIs with targets
5. User Stories - Detailed scenarios with acceptance criteria
6. Functional Requirements - Must/Should/Could/Won't have features
7. Technical Requirements - Architecture, APIs, dependencies
8. AI/ML Specifications - Model requirements, data needs, bias mitigation
9. User Experience - Key flows and interactions
10. Risk Assessment - Potential issues and mitigations
11. Launch Plan - Phased rollout strategy
12. Stakeholder Matrix - RACI chart
13. Appendix - Research summary, competitive analysis

Usage Instructions

Basic Invocation

Create a PRD for [feature name] that solves [problem] for [user segment]

Detailed Invocation

Generate a comprehensive PRD with the following details:
- Feature: AI-powered recommendation engine
- Problem: Users spend too much time searching for relevant products
- Target Users: E-commerce shoppers, focus on returning customers
- Business Goals: Increase conversion rate by 15%, improve average order value
- Include AI/ML requirements and ethical considerations

With Structured Input

Provide JSON input matching the schema above for most detailed results.

Best Practices

• Start with the problem, not the solution - Focus on user needs first
• Be specific about success metrics - Include baseline, target, and timeline
• Document assumptions - Make implicit knowledge explicit
• Include alternatives considered - Show why this approach was chosen
• Address "why now?" - Explain timing and urgency
• Consider edge cases - Don't just focus on happy path
• Plan for failure - Include rollback strategy
• Quantify impact - Use data wherever possible
• Keep it living - PRD should evolve as you learn

Composition with Other Skills

Recommended Workflow

1. Before PRD Creation:

• user-research-analyzer → Extract insights from research
• competitive-analyzer → Understand market positioning
• feature-prioritizer → Validate this should be built now

1. During PRD Creation:

• Use this skill (prd-generator) → Create initial PRD
• ai-ethics-assessor → For AI features, evaluate ethical implications
• metrics-dashboard-builder → Define measurement approach

1. After PRD Creation:

• user-story-generator → Break down into development tickets
• stakeholder-communicator → Generate alignment updates
• gtm-strategy-builder → Plan launch approach

Common Pitfalls to Avoid

• Solution before problem - Jumping to "how" before establishing "why"
• Vague success metrics - "Improve user satisfaction" vs. "Increase NPS from 45 to 60"
• Skipping alternatives - Not documenting why other approaches were rejected
• Ignoring constraints - Technical, resource, or timeline limitations
• Missing dependencies - Other teams, systems, or features required
• Unclear scope - What's in v1 vs. future versions
• No rollback plan - How to handle if feature underperforms
• Stakeholder assumptions - Not validating who needs to approve what

AI/ML Specific Considerations

When generating PRDs for AI features, the skill ensures:

• Model Performance Requirements: Accuracy, latency, throughput targets
• Data Requirements: Training data size, quality, labeling needs
• Bias & Fairness: Evaluation criteria across user demographics
• Explainability: How users understand AI decisions
• Monitoring: Ongoing model performance tracking
• Fallback Behavior: What happens when model fails or is uncertain
• Ethical Guidelines: Privacy, transparency, accountability measures
• Regulatory Compliance: GDPR, AI Act, industry-specific regulations

Python Functions

This skill uses the following Python functions:

generate_prd(input_data: dict) -> str

Main function that orchestrates PRD generation.

Parameters:

• input_data: Dictionary containing feature requirements

Returns: Complete PRD in Markdown format

calculate_opportunity_size(reach: int, impact: float, market_size: float) -> dict

Estimates market opportunity using TAM/SAM/SOM framework.

generate_success_metrics(business_goals: list, baseline_data: dict) -> list

Creates SMART metrics with targets and measurement methods.

extract_user_stories(requirements: list, user_personas: list) -> list

Converts functional requirements into user story format with acceptance criteria.

assess_risks(feature_scope: dict, technical_complexity: str) -> list

Identifies potential risks and suggests mitigation strategies.

create_launch_phases(scope: dict, dependencies: list) -> dict

Designs phased rollout plan based on scope and dependencies.

Output Format Example

# PRD: [Feature Name]

## Executive Summary
[One-page overview with problem, solution, impact, and ask]

## Problem Statement
**Job-to-be-Done**: When [situation], I want to [motivation], so I can [outcome].

**Current Experience**: [Pain points]

**Desired Experience**: [Vision]

## Opportunity Sizing
- **TAM** (Total Addressable Market): [size]
- **SAM** (Serviceable Addressable Market): [size]
- **SOM** (Serviceable Obtainable Market): [size]
- **Expected Impact**: [RICE calculation]

## Success Metrics
| Metric | Baseline | Target | Timeline | Measurement |
|--------|----------|--------|----------|-------------|
| [Metric 1] | [value] | [value] | [date] | [method] |

[... continues with all PRD sections]

Related Documentation

• See user-research-analyzer for analyzing research before writing PRD
• See feature-prioritizer for validating feature should be built
• See ai-ethics-assessor for AI-specific ethical evaluation
• See stakeholder-communicator for sharing PRD with stakeholders

Version: 1.0.0 Last Updated: November 2025 Skill Type: Generative (with Python) Complexity: Advanced