How to Write PRDs for AI Products That Engineering Can Actually Build

AI product requirements break down when they stop at "add AI" and never define the workflow, inputs, outputs, and failure handling.

What an AI PRD Must Clarify

• The user problem and why AI is the right mechanism
• The sources of context, retrieval, and system constraints
• What success looks like for quality, latency, and usability
• What happens when the model is uncertain or wrong
• How the feature will be evaluated before and after launch

Translate Capability Into Product Scope

A strong AI PRD frames the user experience, the data dependencies, the guardrails, and the tradeoffs between accuracy, speed, and cost.

Make Evaluation Part of the Requirement

If you cannot explain how a feature will be tested, benchmarked, and observed in production, the requirement is not ready.