A traditional PRD describes a desired user experience and leaves implementation to engineering. An AI feature spec needs to go further — it must describe the desired model behaviour, the acceptable failure modes, and the quality bar required to ship. Without these, engineering will make assumptions you will not agree with.
What to Add to Your Spec
Behaviour description over UI description
For a traditional feature, you describe what the user sees and clicks. For an AI feature, you describe what the model should do given different types of input. Include examples — good inputs with expected outputs, bad inputs with expected handling.
The quality bar
State explicitly: what accuracy rate is acceptable? What happens when the model is wrong? Who reviews edge cases? This forces a conversation before the first line of code is written rather than after the first production incident.
The evaluation criteria
How will you know when the feature is ready to ship? List the test cases, the success metrics, and the sample size you need to feel confident. “It looked good in demos” is not an evaluation criteria.
The error states
What does the UI do when the model fails, returns a low-confidence output, or takes too long? AI features that ship without designed error states feel broken. Design the failure path as carefully as the success path.
The Conversation the Spec Should Start
A good AI feature spec does not answer all the questions — it surfaces the right disagreements early. When engineers read it and say “this isn’t how the model works,” that is the spec working. You want to have that conversation in planning, not in the week before launch.
A Note on Scope
AI features have a tendency to expand. The initial prompt sounds simple; the edge cases accumulate; the spec grows. Version 1 of any AI feature should do one thing extremely well. Resist the pull toward comprehensiveness. Your users will tell you what to add next.