Mastering the AI-Specific PRD: A Complete Guide

Writing PRDs for AI features requires a fundamentally different approach than traditional software. The non-deterministic nature of AI outputs, the need for continuous evaluation, and the ethical considerations all demand special attention.

The Anatomy of an AI PRD

An effective AI PRD goes beyond the standard sections. It must address:

Model Requirements: What type of model is needed? What are the latency requirements? How will the model be trained and updated?

Data Requirements: What data is needed for training? How will data quality be ensured? What are the privacy implications?

Evaluation Criteria: How will success be measured? What metrics matter beyond accuracy? How will edge cases be handled?

Common Pitfalls to Avoid

Many PMs fall into the trap of treating AI features like traditional software. They specify exact outputs when they should be defining acceptable ranges. They focus on accuracy when user experience metrics matter more.

A Framework for Success

Start with the user problem, not the technology. Define what "good enough" looks like before pursuing perfection. Plan for failure modes from day one. And always, always consider the ethical implications of your AI feature.