PRD Writer - AI Era Product Specifications

This skill creates modern, decision-focused PRDs that work with AI prototyping tools while maintaining strategic clarity. Based on proven practices from leading tech companies including OpenAI.

Core Philosophy

PRDs are about decisions, not documentation.

A great PRD in 2025:

• Makes explicit decisions at every turn
• Contains concrete examples, not vague descriptions
• Lives and evolves with the product
• Works alongside AI prototyping, not against it
• Is short, sharp, and actionable

The fatal flaw of bad PRDs: They say a lot without deciding anything. "Improve engagement" is a hope, not a specification.

Why PRDs Still Matter

Even with AI prototyping tools (Cursor, Replit, v0), PRDs remain critical because prototypes don't specify:

1. Strategic context: How does this fit the overall strategy?
2. Success criteria: What metrics define success?
3. Rollout plan: Who gets this and when?
4. Risk management: What could go wrong and how do we handle it?
5. Non-goals: What are we explicitly NOT doing?

Key insight: When building fast becomes easy (thanks to AI), knowing what to build becomes even more important.

The Modern Product Development Flow

Old flow (linear): PRD → Design → Build → Test

New flow (cyclical): Idea → Quick Prototype → PRD → Refined Prototype → Ship

How PRDs and Prototypes Work Together

Prototypes as discovery tools:

• Use AI tools to mock up 3 different approaches in an afternoon
• Each prototype teaches something about the problem space
• PRD captures learnings and sets direction

PRDs as prototype constraints:

• PRD provides guardrails for prototyping
• Answers: What edge cases? What metrics? How does this fit strategy?

The feedback loop:

• Iterate between prototypes and PRDs multiple times
• Each prototype informs the PRD
• Each PRD update guides the next prototype

Common Failure Modes Without PRDs

Teams that skip PRDs typically:

1. Build something fast that doesn't solve the right problem
2. Build the right thing but can't measure if it worked
3. Ship something that breaks other parts of the product

The PRD is your insurance policy against these failures.

Essential PRD Components

Every great PRD must include these elements:

1. Opportunity Framing

• Core Problem: One-sentence summary of the issue
• Working Hypothesis: One-sentence proposed answer
• Strategy Fit: Which bet/initiative this unlocks now

2. Boundaries

• Scope: What's included
• Non-Goals: What's explicitly excluded (critical for decision-making)

3. Success Measurement

• Offline Golden Set: Test data for validation
• Human Review: Qualitative checks
• Online Metrics: Specific KPIs with thresholds (not "improve X")

4. Rollout Plan

• Exposure: Percentage of traffic or users (specific numbers)
• Duration: Planned test length
• Segments & Ramp Gates: Sequencing criteria and decision points

5. Risk Management

• Detection: How to spot failures
• Fallback & Kill Switch: Recovery mechanisms
• Owners: Who handles incidents

6. Ownership & Action

• Primary Owner: Accountable person/team
• Decision Points: When to revisit/adjust

AI-Specific PRD Requirements

For AI features, add these critical elements:

Behavior Contract with Examples

The defining characteristic of AI PRDs: tons of concrete examples

Include 15-25 labeled examples showing:

• Good responses: What the AI should do
• Bad responses: Common failure modes
• Reject cases: When AI should refuse/defer

Format for each example:

User Input: [specific query or scenario]
Good Response: [desired AI behavior]
Bad Response: [what to avoid]
Reject: [when to refuse]

Principles and Instructions

• Clear guidelines for AI behavior
• Specific risks and how to avoid them
• Guardrails and safety measures

Edge Cases and Red-Team List

• Adversarial inputs
• PII/sensitive data handling
• Performance degradation scenarios
• Code snippets, special characters, multi-language inputs

Reference model: OpenAI's Model Spec is the gold standard - filled with concrete examples, not abstract principles.

The Five-Stage PRD Evolution

Critical principle: Don't treat PRDs as one-and-done. They evolve through the product lifecycle.

Stage 1: Planning (Speclet)

Lightweight exploration document:

• Problem + motivating data (quantitative + 3 user quotes)
• Hypothesis + strategy fit
• Comp set & prior art research
• Open questions & owners

Purpose: Build shared understanding and get alignment to proceed

Stage 2: Kickoff

Decision to build - now add structure:

• Clear in/out of scope
• Napkin mock (be ready to throw away)
• Success metrics + MDE (Minimum Detectable Effect) + guardrails
• Impact sizing model (order-of-magnitude)

Purpose: Set boundaries and success criteria before detailed design

Stage 3: Solution Review

Detailed specification ready for engineering:

• Behavior contract draft + 15-25 examples
• Edge cases + red-team list
• Tracking requirements
• Rollout design v1

Purpose: Engineering can build to this spec

Stage 4: Launch Readiness

Pre-ship checklist completion:

• Offline eval golden set ready
• Human review rubric ready
• Runbook + fallbacks + kill switch wired
• Legal/Security reviewed

Purpose: Safe, measurable launch

Stage 5: Impact Review (Post-Ship)

Learning and iteration:

• Update PRD top with results doc link
• What surprised us? What will we change?
• Annex: add new good/bad/reject examples from real traffic
• Decision: iterate, scale, or retire

Purpose: Close the loop and capture learnings

Writing Process Best Practices

Critical Rule #1: Don't Use LLMs for First Drafts

Why: LLMs create verbose, decision-free documentation that says nothing

Instead:

• Write the first draft yourself with clear decisions
• Use LLM as copilot to improve and finesse
• Think of AI as teammate, not ghostwriter

Critical Rule #2: Be Specific, Not Generic

Bad (vague): "Improve user engagement" Good (specific): "P50 reply time drops ≥10% vs control group"

Bad (generic): "Generate helpful replies" Good (actionable): "For simple questions (<10 words), respond within 2s with contextually relevant suggestions based on last 3 messages"

Bad (hopeful): "Reduce support tickets" Good (measurable): "Decrease returns-related support tickets by 15-20% (from baseline 18% to 14.4-14.8%) measured over 30-day post-implementation window"

Critical Rule #3: Make Decisions, Not Descriptions

Every section should answer a decision:

• Not: "We will test the feature"
• But: "A/B test with 5% user-level randomization for 2 weeks, graduating at p<0.05 with 10%+ metric lift"

PRD Quality Checklist

Before considering a PRD complete, verify:

Strategic Clarity

• Problem statement is one sentence
• Hypothesis is one sentence
• Strategy fit is explicit and current

Measurability

• Success metrics have specific thresholds (not "improve")
• Guardrail metrics are defined
• Graduation criteria are clear

Actionability

• Engineering knows exactly what to build
• Behavior is specified with examples (15-25 for AI features)
• Edge cases are enumerated

Risk Management

• Detection mechanisms are defined
• Fallback strategies exist
• Kill switch is specified with owner

Rollout Precision

• Exposure percentage is specific
• Duration is planned
• Ramp gates have criteria

Decision Quality

• Every "will" has a "how" and "when"
• Non-goals are explicit
• Owners are named

Common PRD Antipatterns

Antipattern 1: Prose Without Decisions

Symptom: Long paragraphs explaining context with no actionable outcomes

Fix: Every paragraph should end with a decision or a specific example

Antipattern 2: Metric Theater

Symptom: "Improve engagement", "Increase satisfaction", "Reduce costs"

Fix: "P50 engagement time increases ≥15%", "NPS increases from 42 to 48+", "Server costs decrease $12K/month"

Antipattern 3: Implementation Fantasy

Symptom: "Start small, then ramp" or "Three-phase approach"

Fix: "Week 1: 5% US users, Week 2: Graduate if p<0.05 and +10% metric, Week 3: Scale to 25%"

Antipattern 4: Vibe-Based Behavior

Symptom: "Generate helpful replies" or "Provide good recommendations"

Fix: 25 labeled examples showing good/bad/reject cases with specific inputs and outputs

Antipattern 5: One-and-Done Documentation

Symptom: PRD written once, never updated, gathering dust

Fix: Update PRD at each stage, link to results, annex learnings from production

AI Prototyping Integration Checklist

When using AI tools in development, ensure:

Before Prototyping

• PRD speclet exists with problem/hypothesis
• Strategic fit is validated
• Success metrics are defined

During Prototyping

• Each prototype tests specific hypothesis from PRD
• Learnings feed back into PRD updates
• Edge cases discovered are documented

After Prototyping

• PRD reflects prototype learnings
• Behavior contract includes examples from prototypes
• Rollout plan accounts for prototype findings

Before Engineering

• PRD is specific enough to build from
• Examples cover major use cases
• Risk mitigation is planned

Output Format Guidelines

When creating PRDs, use this structure:

Document Header

Title: [Feature Name]
Owner: [Name/Team]
Status: [Planning/Kickoff/Solution Review/Launch Ready/Shipped]
Last Updated: [Date]

Section Organization

1. Executive Summary (2-3 sentences max)
2. Opportunity Framing (Problem, Hypothesis, Strategy Fit)
3. Success Measurement (Metrics with thresholds)
4. Scope & Non-Goals (Explicit boundaries)
5. Behavior Specification (Examples for AI features)
6. Rollout Plan (Exposure, duration, gates)
7. Risk Management (Detection, fallbacks, owners)
8. Open Questions (What we don't know yet)

Formatting Best Practices

• Use tables for examples and metrics
• Bold key decisions and thresholds
• Keep paragraphs to 3 sentences max
• Use bullet points for lists, not prose
• Include specific numbers, not ranges without justification

Domain-Specific Guidance

For Traditional Features (Non-AI)

Focus on:

• User flows and interaction patterns
• Technical dependencies and integrations
• Performance requirements with SLAs
• Migration plans if replacing existing functionality

For AI/ML Features

Focus on:

• Behavior contract with 15-25 examples
• Model performance thresholds
• Human review rubrics
• PII and safety considerations
• Fallback to non-AI flows

For Infrastructure/Platform

Focus on:

• SLAs and reliability targets
• Scale requirements with specific traffic projections
• Migration path and backward compatibility
• Monitoring and alerting specifications

For Experiments/A B Tests

Focus on:

• Statistical power calculations
• Contamination prevention strategies
• Minimum detectable effects
• Graduation and rollback criteria

Reviewing Existing PRDs

When reviewing a PRD, assess these dimensions:

Decision Density

• Count decisions per page (aim for 5+)
• Flag vague language ("improve", "enhance", "optimize")
• Identify missing thresholds on metrics

Example Richness (for AI features)

• Count labeled examples (need 15-25)
• Check for edge case coverage
• Verify good/bad/reject cases exist

Strategic Grounding

• Can you trace feature to specific strategy?
• Are non-goals explicit?
• Is opportunity sizing quantified?

Rollout Readiness

• Are exposure percentages specific?
• Do graduation criteria exist?
• Is risk mitigation planned?

Actionability Test

Ask: "Can engineering build from this without asking questions?" If no, the PRD needs more specificity.

Reference Resources

For additional guidance, see:

• references/prd-template.md - Complete template with examples
• references/ai-examples.md - 50+ AI behavior examples
• references/before-after.md - Real PRD transformations
• references/metrics-library.md - Common metrics with thresholds

Key Reminders

1. PRDs are living documents - Update through all five stages
2. Decisions over documentation - Every section should decide something
3. Examples are mandatory - Especially for AI features (15-25 minimum)
4. Specificity wins - "≥10%" not "improve"
5. Work with prototypes - PRDs and prototypes iterate together
6. Don't use LLMs for first drafts - Use them to polish your decisions
7. Non-goals matter - What you're NOT doing is as important as what you are
8. Rollout is precise - Percentages, durations, criteria - not "start small"