Most AI product requirements documents fail because they treat AI features like traditional software features. This template is designed specifically for AI products.
Step 1: Define the user problem and AI hypothesis. What problem does the user have? How does AI solve it better than non-AI approaches? What is your hypothesis about model capabilities? Document assumptions explicitly.
Step 2: Specify AI behavior requirements. Instead of just listing features, define expected AI behavior: What should the AI do when it is confident? What should it do when uncertain? What is the acceptable error rate? When should it escalate to a human?
Step 3: Define data requirements. What training data or knowledge base does the AI need? Where does this data come from? How frequently does it need updating? What are the data privacy requirements?
Step 4: Specify model requirements. Which LLM or ML model should be used? What are the latency requirements? What is the acceptable cost per inference? Are there any compliance constraints on model selection?
Step 5: Define evaluation criteria. How will you measure if the AI is working correctly? Define quantitative metrics (accuracy, latency, cost) and qualitative criteria (user satisfaction, output quality). Include example inputs and expected outputs.
Step 6: Plan for iteration. AI products require more iteration than traditional software. Plan for prompt tuning, model switching, and feedback loop integration in your timeline.
Download the complete AI PRD template with fill-in sections, example completions, and a review checklist at the end of this guide.
