AI Product PRD Template (Free): Model Specs, Evals &

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.

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.

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.

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. ---