An AI PRD needs to account for behavior you can’t fully predict and quality criteria that are subjective and evolve with real usage. That’s because AI is fundamentally different from traditional software: it’s probabilistic, not deterministic. That uncertainty runs through every section of the PRD: how you scope the work, what you require, how you measure success, and what risks you plan for.
Overview
The overview covers the product name, what it does, and why it exists. Describe what users can do and what problem that solves for them.
Goals and Objectives
Be explicit about outcomes and what achieving them means in practice. “Reduce time on task” is a goal. “Users complete the workflow in under three minutes” is something you can measure. Define the difference upfront.
Scope
Phase your scope. Document the core AI capability for Phase 1: a general solution that works broadly. Explicitly plan for a Phase 2 that expands toward more customized, data-driven behavior once you have real usage data.
This iterative and incremental approach matters more for AI products than for traditional software. With deterministic software, you can fully specify behavior upfront because input A always produces output B. With AI, outputs are probabilistic, what “good” looks like is often subjective, and your understanding of quality will shift once real users interact with the model in ways you didn’t anticipate. Requirements that look solid in testing often need revision once you see how the model actually performs in production.
Starting with a general solution also gives you time to build your evaluation framework before you need it to be comprehensive. By the time you’re expanding into Phase 2, you’ll have real data on where the model succeeds and fails, what users actually need, and what good enough means for your use case. That data is what makes the second phase worth building.
Stakeholders
Three groups need to be aligned: those who build it, those who deal with its downstream effects, and those who own the outcomes. Identify each from the start the:
- Core team including product and engineering.
- Supporting functions such as marketing, customer support, and finance.
- Business stakeholders.
Supporting functions are the ones most likely to be looped in late, which is a mistake for AI features. Support needs to understand what the model can and cannot do before customers call with questions about AI-generated content. Finance needs visibility into inference costs, which scale with usage in ways that don’t behave like typical infrastructure costs and can surprise a team that didn’t model them. Marketing needs guardrails around what claims they can make. Loop these teams in early.
Detailed Requirements
Functional requirements need to address what the model is responsible for and everything that shapes how it does that work. Key areas to cover:
- The AI task: Define explicitly what the model is doing. Is it generating content, classifying inputs, summarizing information, making recommendations?
- Additional context (RAG): If the model needs access to your product’s data (documentation, user history, a knowledge base), specify what that retrieval layer looks like and what data it draws from. This affects both output quality and compliance.
- Model selection: Document which model you’re using and why. Model choice affects cost, latency, capability, and what happens when the provider changes pricing or deprecates a version. This is a product decision, not just a technical one.
- UI considerations: Define where the AI feature surfaces in the product, how users trigger it, and how they interact with the output. AI responses vary, take time to generate, and may need to be edited or regenerated. These are different interaction patterns from deterministic features and require explicit design decisions.
- Human-in-the-loop: Define where user review or approval belongs in the workflow. This is not a safety fallback; it is a trust mechanism. Users tend to engage more readily with AI features when they can validate, edit, or override the output before it is acted on. Specify those touchpoints explicitly.
- Feedback mechanism: Build feedback collection in from the start, whether that’s thumbs up/down, correction flows, or explicit ratings. This data improves the model and forms the foundation of your evaluation framework.
Non-functional requirements will vary by product, but at minimum cover:
- Performance: Set latency expectations. For generative AI, streaming output is the primary way to manage perceived latency: users see content appearing rather than waiting for a complete response. Also specify limits on regeneration frequency to manage both experience and cost.
- Scalability: Set usage targets and load test against them. Inference costs and latency can shift significantly as usage grows. You want to understand the limits before your users do.
- Reliability: Define what happens when the model is unavailable. Graceful degradation, disabling the feature cleanly rather than surfacing errors, is better than leaving users with a broken experience.
- Security and compliance: Specify content moderation requirements, what the model should never output, and how you’ll guide users on appropriate use of generated content. This matters especially when generated content can be published, shared externally, or acted on.
Success Metrics
Define two sets of metrics upfront: business metrics and AI metrics.
Business metrics measure user and business outcomes such as adoption rate, feature engagement, user satisfaction scores, and impact on retention or churn. Define baselines where you can.
AI metrics are where most teams under-invest early. At launch, define at minimum a qualitative assessment: have your team review real outputs and rate them. This vibe check is your starting point. It needs to evolve into a more formal framework that tracks model performance against defined quality criteria, error and hallucination rates, response latency, and inference cost per interaction. Cost monitoring is easy to skip early and painful to retrofit. Include it from day one.
Dependencies
List technical dependencies such as model APIs, data pipelines, and retrieval infrastructure alongside operational ones such as content moderation tooling, monitoring infrastructure, and legal review of AI content policies. Flag anything that could block launch or degrade performance.
Assumptions
Document your assumptions about model quality, user behavior, and data availability. The most consequential assumptions for AI features are usually about user behavior: specifically whether users will engage with AI output the way you expect and act as the intended check on what the model produces.
Risks
AI features amplify certain risks and introduce new ones. Common ones to plan for:
- Cost: Document your inference cost assumptions and the scenarios that could cause them to spike. Inference costs scale with usage in ways that can surprise, so model a range of usage scenarios before committing to pricing or infrastructure.
- Content quality: The model will produce bad output at some point. Define what “bad” means for your use case and how you’ll catch it before users do.
- Prompt hacking: This covers two related threats: users crafting inputs to manipulate model behavior, and the system prompt being exposed to users or competitors. Specify input validation and how you’ll protect the integrity of your prompts.
- Moderation: Define what the model must never produce and whether violations are caught automatically or escalated for human review.
AI fails silently. Robust monitoring is not optional. You need to know when something is going wrong before your users tell you.
Timeline
Include AI-specific workstreams as explicit items in your timeline. Prompt development and evaluation setup need to run in parallel with feature development.
A Living Document
An AI PRD creates alignment and guidance for a team building something whose behavior they can’t fully predict upfront. And because a model upgrade can change your product’s behavior without anyone touching the code, it’s a document that needs to stay current throughout the life of the feature, not just at kickoff.
