Great question. When you’re asking an AI to draft a PRD, think of it the same way you’d guide a new team member. If you’re vague, they’ll fill in gaps with assumptions, and that usually means rework.
The more you set context, the more usable the output becomes. I’ve found the sweet spot is giving the AI both constraints and examples. For instance, tell it the audience (engineers, stakeholders), the level of detail expected, and the format you prefer.
Here’s a structure that works well:
- Context: Problem statement, why the feature matters, who it’s for
- Scope: What’s in, what’s out, any constraints
- Details: Functional requirements, edge cases, success metrics
- Style: Specify format, level of formality, and whether you want tables, bullets, or narrative sections
A common mistake is overloading the prompt with raw notes and expecting the AI to “sort it out.” Instead, break the request into clear chunks.
Ask for a first draft in a specific format, then refine by iterating with feedback. That way, the AI becomes more of a collaborative partner than a one-shot generator.