AI PM Templates
AI Feature PRD Template: Write Better AI Product Requirements
A comprehensive, copy-paste ready PRD template designed specifically for AI features with sections for model requirements, data needs, ethical considerations, and rollout strategy.
Institute of AI PM
December 10, 2025
12 min read
Traditional PRDs fall short for AI features. They lack sections for model performance requirements, data dependencies, failure modes, and the probabilistic nature of AI outputs. This template bridges that gap with a comprehensive structure used by top AI product teams at companies like OpenAI, Anthropic, and Google DeepMind.
How to Use This Template
- 1. Copy the complete template below into your documentation tool
- 2. Fill in each section, deleting guidance text as you go
- 3. Not all sections apply to every feature - mark N/A where appropriate
- 4. Share with engineering early for technical feasibility review
Why AI Features Need a Different PRD
AI features introduce unique challenges that traditional PRDs don't address. Understanding these differences helps you write more complete requirements.
Traditional PRD vs AI PRD
Traditional Software PRD
- Deterministic requirements
- Binary pass/fail acceptance
- Static feature behavior
- Clear input/output specs
- One-time QA testing
AI Feature PRD
- Probabilistic requirements
- Performance thresholds
- Behavior may evolve
- Edge cases and failure modes
- Ongoing monitoring required
Complete AI Feature PRD Template
Copy this entire template and customize for your feature. Each section includes guidance notes in italics that you should replace with your actual content.
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AI FEATURE PRD TEMPLATE
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DOCUMENT INFO
─────────────────────────────────────────────────────────────────
Feature Name: [Feature name]
Author: [Your name]
Created: [Date]
Last Updated: [Date]
Status: [Draft | In Review | Approved | In Development]
Version: [1.0]
REVIEWERS & APPROVERS
─────────────────────────────────────────────────────────────────
- Engineering Lead: [Name] - [Status]
- ML/AI Lead: [Name] - [Status]
- Design Lead: [Name] - [Status]
- Data Science: [Name] - [Status]
- Legal/Compliance: [Name] - [Status]
═══════════════════════════════════════════════════════════════════
SECTION 1: EXECUTIVE SUMMARY
═══════════════════════════════════════════════════════════════════
1.1 ONE-LINER
─────────────────────────────────────────────────────────────────
[One sentence describing what this feature does]
Example: "AI-powered email response suggestions that draft contextual
replies based on email content and user writing style."
1.2 PROBLEM STATEMENT
─────────────────────────────────────────────────────────────────
What problem are we solving?
[Describe the user pain point in 2-3 sentences]
Who experiences this problem?
[Describe the target user segment]
How do users solve this today?
[Current workarounds or competitor solutions]
Why is AI the right solution?
[Explain why AI is needed vs traditional software]
1.3 SUCCESS METRICS
─────────────────────────────────────────────────────────────────
Primary Metric:
- [Metric name]: [Current baseline] → [Target] by [Date]
Secondary Metrics:
- [Metric 2]: [Baseline] → [Target]
- [Metric 3]: [Baseline] → [Target]
Guardrail Metrics (must not regress):
- [Metric]: Must stay above [threshold]
1.4 TIMELINE & MILESTONES
─────────────────────────────────────────────────────────────────
| Milestone | Target Date | Dependencies |
|------------------------|-------------|---------------------|
| PRD Approved | [Date] | - |
| Data Collection Start | [Date] | Data team capacity |
| Model v1 Complete | [Date] | Training data ready |
| Internal Alpha | [Date] | Model v1 |
| Limited Beta | [Date] | Alpha feedback |
| General Availability | [Date] | Beta success |
═══════════════════════════════════════════════════════════════════
SECTION 2: USER EXPERIENCE
═══════════════════════════════════════════════════════════════════
2.1 USER STORIES
─────────────────────────────────────────────────────────────────
Primary User Story:
As a [user type], I want to [action] so that [benefit].
Additional User Stories:
- As a [user], I want [feature] so that [benefit]
- As a [user], I want [feature] so that [benefit]
2.2 USER FLOW
─────────────────────────────────────────────────────────────────
[Describe step-by-step user journey]
1. User [action/trigger]
2. System [AI processing step]
3. User sees [output/interface]
4. User can [interaction options]
5. System [feedback/learning loop]
2.3 INTERFACE REQUIREMENTS
─────────────────────────────────────────────────────────────────
Input Interface:
- [How users provide input to the AI]
- [Input constraints and validation]
Output Interface:
- [How AI results are displayed]
- [Confidence indicators if applicable]
Feedback Mechanisms:
- [How users can correct/improve outputs]
- [How feedback is captured for model improvement]
2.4 EDGE CASES & ERROR STATES
─────────────────────────────────────────────────────────────────
| Scenario | Expected Behavior |
|-----------------------------|--------------------------------|
| AI confidence too low | [Fallback behavior] |
| No relevant data available | [Empty state messaging] |
| Model timeout/failure | [Error handling] |
| Inappropriate input | [Content filtering response] |
| Rate limit exceeded | [Throttling message] |
═══════════════════════════════════════════════════════════════════
SECTION 3: AI/ML REQUIREMENTS
═══════════════════════════════════════════════════════════════════
3.1 MODEL APPROACH
─────────────────────────────────────────────────────────────────
Recommended Approach: [Build | Buy | Fine-tune | Prompt Engineering]
Justification:
[Why this approach? Consider: cost, time, performance needs,
data availability, competitive differentiation]
Model Type: [Classification | Generation | Recommendation | etc.]
Base Model (if applicable): [Model name and version]
3.2 PERFORMANCE REQUIREMENTS
─────────────────────────────────────────────────────────────────
Accuracy Metrics:
| Metric | Minimum | Target | Stretch |
|-----------------|---------|--------|---------|
| Accuracy | [X]% | [Y]% | [Z]% |
| Precision | [X]% | [Y]% | [Z]% |
| Recall | [X]% | [Y]% | [Z]% |
| F1 Score | [X] | [Y] | [Z] |
Latency Requirements:
- P50 latency: [X] ms
- P95 latency: [Y] ms
- P99 latency: [Z] ms
Throughput:
- Expected QPS: [X] queries per second
- Peak QPS: [Y] queries per second
3.3 DATA REQUIREMENTS
─────────────────────────────────────────────────────────────────
Training Data:
- Source: [Where does training data come from?]
- Volume: [How much data is needed?]
- Format: [Data structure/format]
- Labeling: [Manual | Automated | Semi-supervised]
- Timeline: [When will data be ready?]
Inference Data:
- Input format: [Structure of runtime inputs]
- Required fields: [List required fields]
- Optional fields: [List optional enrichments]
Data Privacy:
- PII handling: [How is personal data handled?]
- Data retention: [How long is data stored?]
- User consent: [What consent is required?]
3.4 EVALUATION STRATEGY
─────────────────────────────────────────────────────────────────
Offline Evaluation:
- Test dataset: [Description of held-out test set]
- Evaluation cadence: [How often?]
- Benchmark comparisons: [What baselines to compare against?]
Online Evaluation:
- A/B test design: [Control vs treatment]
- Success criteria: [What determines winner?]
- Sample size: [Statistical requirements]
- Test duration: [How long to run?]
═══════════════════════════════════════════════════════════════════
SECTION 4: ETHICAL CONSIDERATIONS
═══════════════════════════════════════════════════════════════════
4.1 BIAS & FAIRNESS
─────────────────────────────────────────────────────────────────
Potential Bias Sources:
- [Data bias risk 1]
- [Data bias risk 2]
- [Algorithmic bias risk]
Mitigation Strategies:
- [How will each bias be addressed?]
Fairness Metrics:
- [What metrics will track fairness across groups?]
4.2 TRANSPARENCY & EXPLAINABILITY
─────────────────────────────────────────────────────────────────
User Disclosure:
- [How will users know AI is involved?]
- [What explanations are provided for outputs?]
Confidence Communication:
- [How is uncertainty conveyed to users?]
4.3 SAFETY & MISUSE PREVENTION
─────────────────────────────────────────────────────────────────
Potential Misuse Scenarios:
- [Misuse case 1]: [Mitigation]
- [Misuse case 2]: [Mitigation]
Content Safety:
- [Filtering mechanisms]
- [Human review triggers]
Rate Limiting:
- [Abuse prevention measures]
4.4 HUMAN OVERSIGHT
─────────────────────────────────────────────────────────────────
Human-in-the-Loop Requirements:
- [When is human review required?]
- [Escalation paths]
Override Capabilities:
- [How can humans override AI decisions?]
═══════════════════════════════════════════════════════════════════
SECTION 5: TECHNICAL ARCHITECTURE
═══════════════════════════════════════════════════════════════════
5.1 SYSTEM DESIGN
─────────────────────────────────────────────────────────────────
[High-level architecture description]
Components:
- [Component 1]: [Purpose]
- [Component 2]: [Purpose]
- [Component 3]: [Purpose]
5.2 INFRASTRUCTURE REQUIREMENTS
─────────────────────────────────────────────────────────────────
Compute:
- Training: [GPU/TPU requirements]
- Inference: [Serving infrastructure]
Storage:
- Model artifacts: [Size and storage]
- Feature store: [Requirements]
- Logging: [Volume estimates]
Cost Estimates:
- Training (one-time): $[X]
- Inference (monthly): $[X]
- Storage (monthly): $[X]
5.3 INTEGRATION POINTS
─────────────────────────────────────────────────────────────────
| System | Integration Type | Data Exchanged |
|---------------------|------------------|-------------------|
| [System 1] | [API/Event/etc] | [Data description]|
| [System 2] | [API/Event/etc] | [Data description]|
5.4 MONITORING & OBSERVABILITY
─────────────────────────────────────────────────────────────────
Model Performance Monitoring:
- [Metrics to track]
- [Alert thresholds]
- [Dashboard requirements]
Data Quality Monitoring:
- [Input data drift detection]
- [Feature distribution tracking]
Operational Monitoring:
- [Latency tracking]
- [Error rate monitoring]
- [Cost tracking]
═══════════════════════════════════════════════════════════════════
SECTION 6: ROLLOUT STRATEGY
═══════════════════════════════════════════════════════════════════
6.1 LAUNCH PHASES
─────────────────────────────────────────────────────────────────
Phase 1 - Internal Alpha:
- Audience: [Internal users/team]
- Duration: [X weeks]
- Success criteria: [What must be true to proceed?]
- Rollback trigger: [What causes rollback?]
Phase 2 - Limited Beta:
- Audience: [X% of users / specific segment]
- Duration: [X weeks]
- Success criteria: [Metrics thresholds]
- Rollback trigger: [Conditions]
Phase 3 - General Availability:
- Audience: [All users]
- Ramp schedule: [X% → Y% → 100% over Z days]
6.2 FEATURE FLAGS
─────────────────────────────────────────────────────────────────
| Flag Name | Purpose |
|----------------------------|--------------------------------|
| [feature_ai_enabled] | Master kill switch |
| [feature_ai_new_model] | Model version control |
| [feature_ai_percentage] | Rollout percentage |
6.3 ROLLBACK PLAN
─────────────────────────────────────────────────────────────────
Rollback Triggers:
- [Automatic]: [Condition that triggers auto-rollback]
- [Manual]: [Conditions requiring human decision]
Rollback Procedure:
1. [Step 1]
2. [Step 2]
3. [Step 3]
Communication Plan:
- [Who to notify and how]
═══════════════════════════════════════════════════════════════════
SECTION 7: DEPENDENCIES & RISKS
═══════════════════════════════════════════════════════════════════
7.1 DEPENDENCIES
─────────────────────────────────────────────────────────────────
| Dependency | Owner | Status | Risk Level |
|----------------------|--------------|-----------|------------|
| [Dependency 1] | [Team/Name] | [Status] | [H/M/L] |
| [Dependency 2] | [Team/Name] | [Status] | [H/M/L] |
7.2 RISKS & MITIGATIONS
─────────────────────────────────────────────────────────────────
| Risk | Likelihood | Impact | Mitigation |
|-------------------------|------------|--------|----------------|
| Model accuracy too low | [H/M/L] | [H/M/L]| [Plan] |
| Data not available | [H/M/L] | [H/M/L]| [Plan] |
| Latency too high | [H/M/L] | [H/M/L]| [Plan] |
| User adoption low | [H/M/L] | [H/M/L]| [Plan] |
═══════════════════════════════════════════════════════════════════
SECTION 8: APPENDIX
═══════════════════════════════════════════════════════════════════
8.1 GLOSSARY
─────────────────────────────────────────────────────────────────
[Term 1]: [Definition]
[Term 2]: [Definition]
8.2 REFERENCES
─────────────────────────────────────────────────────────────────
- [Link to design mocks]
- [Link to technical design doc]
- [Link to data requirements doc]
- [Link to competitive analysis]
8.3 CHANGE LOG
─────────────────────────────────────────────────────────────────
| Version | Date | Author | Changes |
|---------|------------|-----------|----------------------------|
| 1.0 | [Date] | [Name] | Initial draft |
| 1.1 | [Date] | [Name] | [Summary of changes] |
═══════════════════════════════════════════════════════════════════Section-by-Section Guidance
Writing Strong Success Metrics
AI features need more nuanced success metrics than traditional features. Include both model performance metrics and business impact metrics.
Good vs Bad Metrics Examples
Weak Metrics
- "Improve user satisfaction"
- "Model should be accurate"
- "Reduce support tickets"
Strong Metrics
- "Increase CSAT from 4.1 to 4.5 by Q2"
- "Achieve 92% precision at 85% recall"
- "Reduce AI-related tickets by 40%"
Defining Performance Requirements
Work with your ML team to establish realistic performance targets. Start with minimum viable thresholds, then define target and stretch goals.
PERFORMANCE THRESHOLD FRAMEWORK ================================ Minimum Viable (Launch Blocker): - The absolute minimum for launch - Below this = do not ship - Example: 80% accuracy, P95 < 2s Target (Expected at Launch): - What you're aiming for at GA - Balanced performance/timeline - Example: 88% accuracy, P95 < 500ms Stretch (Post-Launch Optimization): - Aspirational future state - Guides long-term roadmap - Example: 95% accuracy, P95 < 200ms
Common PRD Mistakes to Avoid
Top 5 AI PRD Mistakes
1. Skipping the Ethics Section
Every AI feature has ethical implications. Even "simple" features can cause harm. Always complete this section.
2. Vague Performance Requirements
"Should be accurate" isn't a requirement. Specify exact thresholds with measurement methodology.
3. Ignoring Failure Modes
AI will fail. Document expected failures and graceful degradation strategies.
4. Missing Data Requirements
No data = no AI. Be specific about data sources, volumes, and quality requirements.
5. No Monitoring Plan
AI models degrade over time. Plan for ongoing monitoring from day one.
PRD Completion Checklist
Before submitting your PRD for review, verify all critical sections are complete.
AI FEATURE PRD CHECKLIST ======================== MUST HAVE (Launch Blocker) ───────────────────────────────────────────────────────────────── [ ] Problem statement clearly articulates user pain [ ] Success metrics are quantified with baselines [ ] Performance requirements have specific thresholds [ ] Data requirements are documented with sources [ ] Ethics section completed (bias, safety, transparency) [ ] Rollback plan is documented [ ] Engineering and ML leads have reviewed SHOULD HAVE (Quality Bar) ───────────────────────────────────────────────────────────────── [ ] User stories cover primary and secondary use cases [ ] Edge cases and error states are documented [ ] Monitoring and alerting plan is defined [ ] Cost estimates for training and inference [ ] A/B test design is specified [ ] Legal/compliance review completed NICE TO HAVE (Excellence) ───────────────────────────────────────────────────────────────── [ ] Competitive analysis referenced [ ] Long-term roadmap considerations [ ] Localization requirements [ ] Accessibility considerations for AI outputs [ ] Customer research linked
Example: Smart Reply Feature PRD
Here's a condensed example of how key sections might be filled out for an email smart reply feature.
EXAMPLE: SMART REPLY PRD (Condensed) ==================================== EXECUTIVE SUMMARY ───────────────────────────────────────────────────────────────── Feature Name: Smart Reply Suggestions One-Liner: AI-powered one-click reply suggestions for incoming emails Problem Statement: Users spend 2+ hours daily writing routine email responses. 68% of emails could be answered with short, standard replies but composing even simple responses creates cognitive load and interrupts workflow. Success Metrics: - Primary: Reply composition time reduced from 45s → 8s (82% reduction) - Secondary: Smart reply adoption rate > 25% of all replies - Guardrail: User satisfaction must stay above 4.2/5 AI/ML REQUIREMENTS ───────────────────────────────────────────────────────────────── Approach: Fine-tune base LLM on company email corpus Base Model: GPT-4-turbo Performance Requirements: | Metric | Minimum | Target | Stretch | |------------------|---------|--------|---------| | Response quality | 3.5/5 | 4.2/5 | 4.5/5 | | Tone accuracy | 75% | 85% | 92% | | P95 latency | 2000ms | 800ms | 400ms | Data Requirements: - 500K email/reply pairs for fine-tuning - User writing samples for personalization - Anonymized, with PII stripped ETHICAL CONSIDERATIONS ───────────────────────────────────────────────────────────────── Bias Risks: - Formal/informal tone bias based on recipient name - Mitigation: Tone normalization, bias testing by demographic Transparency: - Label suggestions as "AI-generated" - User can disable feature entirely Safety: - Block suggestions for sensitive topics (legal, HR, health) - Human review for flagged content patterns
Key Takeaways
- AI PRDs need more than traditional PRDs - Include model performance, data requirements, ethics, and monitoring sections.
- Quantify everything - Vague requirements lead to misaligned expectations. Be specific with numbers and thresholds.
- Plan for failure - Document edge cases, error states, and rollback procedures before you ship.
- Ethics is not optional - Every AI feature has potential for harm. Address bias, transparency, and safety proactively.
- Involve engineering early - AI feasibility depends on data and infrastructure. Get technical review before finalizing.