AI-POWEREDPRO⏱️ 45 min

AI Product PRD Template

A product requirements document template designed specifically for AI-powered products, covering model requirements, evaluation criteria, hallucination tolerance, data pipelines, and fallback behaviors.

By Tim Adair• Last updated 2026-02-09

What This Template Does

Building AI products introduces requirements categories that traditional PRDs never address: model selection and performance benchmarks, hallucination tolerance thresholds, data pipeline dependencies, graceful degradation when models fail, and evaluation frameworks that go far beyond standard acceptance criteria. A generic PRD template leaves these critical dimensions unspecified, which leads to misalignment between product, engineering, and data science teams.

This template provides a structured framework purpose-built for AI product requirements. Every section includes specific fields, decision prompts, and examples drawn from real-world AI product development. It covers the full lifecycle from problem definition through model requirements, data needs, evaluation criteria, fallback behaviors, and post-launch monitoring.

Direct Answer

An AI Product PRD is a requirements document that extends a traditional PRD with AI-specific sections: model performance criteria, data requirements and pipelines, hallucination and safety tolerances, evaluation methodology, fallback behavior specifications, and ongoing monitoring requirements. This template provides the complete structure to write one.

Template Structure

1. Product Overview and AI Problem Framing

Purpose: Establish the product context and articulate why AI is the right approach -- not just what you are building, but why a deterministic or rule-based solution is insufficient.

Fields to complete:

## Product Overview

**Product Name**: [Name of the AI product or feature]
**Product Owner**: [Name and role]
**Target Launch Date**: [Date]
**AI/ML Lead**: [Name -- the data scientist or ML engineer who owns the model]

### Problem Statement
[2-3 sentences describing the user problem this product solves]

### Why AI Is Required
- [ ] Problem involves pattern recognition at scale
- [ ] Input data is unstructured (text, images, audio)
- [ ] Solution requires personalization across many dimensions
- [ ] Problem space is too large for hand-crafted rules
- [ ] Requirements include natural language understanding or generation

### AI Approach
**Model Type**: [Classification / Generation / Extraction / Recommendation / Other]
**Primary Technique**: [LLM prompting / Fine-tuned model / RAG / Traditional ML / Hybrid]
**Build vs. Buy**: [Using third-party API / Training custom model / Fine-tuning existing model]

2. User Requirements and Interaction Design

Purpose: Define how users interact with the AI component, what inputs they provide, what outputs they expect, and how they should perceive the AI role.

Fields to complete:

## User Requirements

### Target Users
| User Segment | Use Case | AI Interaction Model |
|-------------|----------|---------------------|
| [Segment 1] | [What they do with the AI] | [Chat / Autocomplete / Background / Recommendations] |
| [Segment 2] | [What they do with the AI] | [Chat / Autocomplete / Background / Recommendations] |

### User Input Specification
- **Input type**: [Free text / Structured form / File upload / Voice / Combination]
- **Average input length**: [Characters / tokens / file size]
- **Input language(s)**: [English only / Multilingual -- list languages]
- **Input validation rules**: [What inputs should be rejected or flagged]

### Expected Output Specification
- **Output type**: [Text / Structured data / Classification label / Score / Image]
- **Output length**: [Expected range]
- **Output format**: [Markdown / JSON / Plain text / Visual]
- **Confidence display**: [Should the UI show confidence scores? Yes/No]

### AI Transparency Requirements
- [ ] Users must know they are interacting with AI
- [ ] AI-generated content must be visually distinguished from human content
- [ ] Users can see the sources or reasoning behind AI outputs
- [ ] Users can provide feedback on AI outputs (thumbs up/down, corrections)
- [ ] Users can override or edit AI outputs before they take effect

3. Model Requirements and Performance Criteria

Purpose: Specify the technical requirements for the AI model, including performance benchmarks, latency constraints, and cost parameters. This section is the contract between product and data science.

Fields to complete:

## Model Requirements

### Performance Benchmarks

| Metric | Minimum Acceptable | Target | Measurement Method |
|--------|-------------------|--------|-------------------|
| Accuracy / Correctness | [e.g., 85%] | [e.g., 92%] | [Human eval / Automated test suite] |
| Precision | [e.g., 80%] | [e.g., 90%] | [Against labeled dataset] |
| Recall | [e.g., 75%] | [e.g., 88%] | [Against labeled dataset] |
| Latency (p50) | [e.g., < 2s] | [e.g., < 500ms] | [End-to-end measurement] |
| Latency (p99) | [e.g., < 5s] | [e.g., < 2s] | [Tail latency measurement] |
| Throughput | [e.g., 100 req/s] | [e.g., 500 req/s] | [Sustained load test] |
| Cost per request | [e.g., < $0.05] | [e.g., < $0.01] | [API cost + compute] |

### Model Selection Criteria
- **Candidate models**: [List 2-3 models under consideration with rationale]
- **Selection criteria priority**: [Rank: accuracy, latency, cost, context window, licensing]
- **Context window requirement**: [Minimum tokens needed for your use case]
- **Fine-tuning feasibility**: [Is fine-tuning required? On what data?]

### Hallucination Tolerance

| Category | Description | Tolerance Level | Mitigation |
|----------|-------------|----------------|------------|
| Factual errors | Model states incorrect facts | [Zero / Low / Medium] | [Grounding, RAG, citations] |
| Fabricated sources | Model invents references | [Zero / Low] | [Source verification, allowlisting] |
| Inconsistency | Model contradicts itself | [Low / Medium] | [Context management, memory] |
| Extrapolation | Unsupported inferences | [Low / Medium / High] | [Prompt constraints, guardrails] |
| Off-topic responses | Answers outside scope | [Zero / Low] | [Topic boundaries, system prompts] |

**Hard constraints** (any violation blocks launch):
- [ ] [e.g., Model must never generate medical advice]
- [ ] [e.g., Model must never fabricate customer data]
- [ ] [e.g., Model must refuse requests outside product scope]

4. Data Requirements

Purpose: Specify what data the model needs, where it comes from, how it is processed, and what governance requirements apply.

Fields to complete:

## Data Requirements

### Training Data (if fine-tuning)
| Dataset | Source | Size | Format | Sensitivity | Status |
|---------|--------|------|--------|-------------|--------|
| [Dataset 1] | [Internal / Public / Licensed] | [Records] | [JSON/CSV] | [PII/PHI/Public] | [Available / Needs collection] |

### Runtime Data (for inference / RAG)
| Data Source | Update Frequency | Latency Requirement | Fallback if Unavailable |
|------------|-----------------|--------------------|-----------------------|
| [Source 1] | [Real-time / Hourly / Daily] | [< Xms] | [Use cached / Degrade gracefully] |

### Data Pipeline Requirements
- **Ingestion**: [How data enters the system]
- **Processing**: [Cleaning, chunking, embedding -- what transformations are needed]
- **Storage**: [Vector DB / Feature store / Cache]
- **Refresh cadence**: [How often data is updated]
- **Data quality checks**: [Automated validation before data reaches the model]

### Data Governance
- [ ] All training data reviewed for PII and sensitive content
- [ ] Data usage complies with source licensing terms
- [ ] Data retention policies defined and documented
- [ ] Users can request deletion of their data from training sets
- [ ] Data lineage is traceable from source to model input

5. Fallback and Degradation Behaviors

Purpose: Define what happens when the AI component fails, times out, produces low-confidence results, or encounters edge cases.

Fields to complete:

## Fallback Behaviors

### Failure Modes and Responses

| Failure Mode | Detection Method | User Experience | System Response |
|-------------|-----------------|-----------------|-----------------|
| Model timeout | Latency monitor | [Show loading then fallback] | [Retry once then cached/default] |
| Low confidence | Confidence threshold | [Show disclaimer / Rephrase] | [Log for review, flag for human] |
| Model error | HTTP status / Error code | [Friendly error message] | [Retry with backoff then escalate] |
| Rate limit exceeded | 429 / Queue depth | [Queue request with ETA] | [Throttle, queue, or shed load] |
| Content violation | Safety classifier | [Refuse and explain why] | [Log incident, do not retry] |
| Nonsensical output | Validation rules | [Do not display, ask to retry] | [Log anomaly, alert on-call] |

### Graceful Degradation Tiers
1. **Full capability**: AI model responds within latency and quality targets
2. **Reduced capability**: [What works without the primary model?]
3. **Manual fallback**: [What non-AI experience does the user see?]
4. **Service unavailable**: [What message and recovery options appear?]

### Circuit Breaker Rules
- **Trip threshold**: [e.g., 5 consecutive failures or error rate > 10% over 5 minutes]
- **Recovery**: [e.g., Half-open after 60 seconds, test with 10% traffic]
- **Notification**: [Who gets alerted when the circuit breaker trips]

6. Evaluation Framework

Purpose: Define how you will evaluate the AI before launch and on an ongoing basis.

Fields to complete:

## Evaluation Framework

### Pre-Launch Evaluation
| Evaluation Type | Method | Dataset Size | Pass Criteria | Owner |
|----------------|--------|-------------|---------------|-------|
| Accuracy benchmarking | [Automated test suite] | [N test cases] | [> X% accuracy] | [Name] |
| Human evaluation | [Expert review] | [N samples] | [> X% acceptable] | [Name] |
| Adversarial testing | [Red team prompts] | [N attack vectors] | [0 critical failures] | [Name] |
| Latency testing | [Load test] | [Duration] | [p99 < Xs] | [Name] |
| Bias evaluation | [Demographic segments] | [N per segment] | [< X% variance] | [Name] |

### Post-Launch Monitoring
| Metric | Collection Method | Alert Threshold |
|--------|------------------|----------------|
| Accuracy (ongoing) | [User feedback + sampling] | [Drop > X% from baseline] |
| Latency (p50/p99) | [APM tool] | [p99 > Xs] |
| Error rate | [Log aggregation] | [> X% over Y minutes] |
| User satisfaction | [Thumbs up/down ratio] | [< X% positive] |
| Cost per request | [Billing API] | [> $X per request] |

### Evaluation Cadence
- **Daily**: Automated metrics review (latency, error rate, cost)
- **Weekly**: Sample N outputs for human quality review
- **Monthly**: Full evaluation suite rerun on updated test set
- **Quarterly**: Bias audit and comprehensive model review

7. Launch Criteria and Rollout Plan

Purpose: Define the go/no-go criteria for launch and the rollout strategy.

Fields to complete:

## Launch Plan

### Go / No-Go Criteria
- [ ] All performance benchmarks met on evaluation dataset
- [ ] Adversarial testing completed with zero critical failures
- [ ] Fallback behaviors tested and verified
- [ ] Monitoring and alerting configured and tested
- [ ] Data privacy review completed and approved
- [ ] Safety review completed and approved
- [ ] Cost projections reviewed and within budget
- [ ] Rollback procedure documented and tested

### Rollout Phases
| Phase | Audience | Duration | Success Criteria | Rollback Trigger |
|-------|----------|----------|-----------------|-----------------|
| Internal dogfood | [Team] | [1-2 weeks] | [Feedback positive] | [Critical bugs] |
| Limited beta | [X% of users] | [2-4 weeks] | [Metrics in targets] | [Error rate > X%] |
| Expanded rollout | [X% of users] | [2-4 weeks] | [Metrics stable] | [Cost spikes] |
| General availability | [All users] | [Ongoing] | [All KPIs green] | [Per circuit breaker] |

### Rollback Procedure
1. [How to disable the AI feature without downtime]
2. [What experience users see when rolled back]
3. [Who has authority to trigger rollback]
4. [Communication plan for users if feature is rolled back]

How to Use This Template

  • Start with Section 1 (Product Overview) to align your team on whether AI is the right approach. If you cannot articulate why a rules-based solution is insufficient, reconsider whether you need AI at all.
  • Complete Sections 2 and 3 (User Requirements and Model Requirements) with your product team and AI/ML lead together. These sections are the contract between product and engineering.
  • Work with your data team on Section 4 (Data Requirements). Data availability and quality are the most common blockers for AI products. Identify data gaps early.
  • Design fallback behaviors (Section 5) before you build the happy path. Users will encounter failures, and the experience during failure defines trust in your AI product.
  • Define your evaluation framework (Section 6) before model development begins. If you do not know how you will measure success, you cannot know when you have achieved it.
  • Set clear launch criteria (Section 7) and commit to incremental rollout. No AI product should launch to 100% of users on day one.
  • Review the completed PRD with stakeholders from product, engineering, data science, design, legal, and security. AI products have a wider blast radius than traditional features.

    • Tips for Best Results

  • Write the hallucination tolerance section with your most skeptical stakeholder. If legal, compliance, or customer success is not comfortable with the tolerance levels, you will discover this at the worst possible time. Involve them early.
  • Set cost budgets before model selection. It is easy to fall in love with the most capable model. Define your cost-per-request ceiling first, then find the best model within that budget.
  • Build your evaluation dataset before you build the product. A curated set of test cases -- including adversarial and edge cases -- is the most valuable artifact in AI product development.
  • Define good enough explicitly. State what performance level is acceptable for launch, the target for three months post-launch, and what is aspirational. Without these tiers, teams either over-polish or under-deliver.
  • Plan for model updates from the start. If using a third-party model, specify how you will evaluate and adopt new versions, including regression testing against your evaluation suite.
  • Document what the AI should refuse to do. Defining boundaries is as important as defining capabilities. List request categories the AI should decline and specify the refusal experience for users.

    • Key Takeaways

  • AI products require hallucination tolerance, fallback behaviors, data pipelines, and continuous evaluation that traditional PRDs do not cover
  • Define your evaluation framework and success metrics before model development begins
  • Fallback behaviors are not an afterthought -- design them alongside the happy path
  • Always plan for incremental rollout with clear rollback procedures
  • Cross-functional review is essential because AI products touch product, engineering, data science, legal, and security

    • About This Template

    Created by: Tim Adair

    Last Updated: 2/9/2026

    Version: 1.0.0

    License: Free for personal and commercial use

    Frequently Asked Questions

    How is an AI PRD different from a regular PRD?+
    A traditional PRD defines deterministic requirements -- given input X, produce output Y. An AI PRD must also define acceptable ranges of output quality, hallucination tolerances, fallback behaviors, data requirements, and ongoing evaluation frameworks. The probabilistic nature of AI means you are specifying acceptable ranges rather than exact behaviors.
    Who should own the AI PRD?+
    The product manager owns the document, but Sections 3 and 4 should be co-authored with the AI/ML lead. Section 6 should be reviewed by QA and data science together. Legal and security should review Sections 3 and 5.
    When should I write the AI PRD?+
    Before any model development begins. The most expensive mistake in AI product development is building a model before the product requirements are clear. Use this template to align on what success looks like before writing code.
    What if I am using a third-party API rather than a custom model?+
    The template still applies. Sections 3 and 4 shift focus from training requirements to API evaluation criteria and integration architecture. The fallback and evaluation sections become even more important because you have less control over the model itself. ---

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