AI killed the 10 page PRD. But the PRD isn’t dead.
Here’s how to write PRDs in the AI age:
The traditional 10-page PRD filled with endless requirements and technical specifications is becoming obsolete. Companies like Google are shifting to a build-first culture where teams prototype rapidly and iterate based on real user feedback rather than lengthy planning documents. And honestly, it’s about time.
AI prototyping tools like Claude, Cursor, and v0 have completely changed the game, allowing product managers to build functional prototypes in hours instead of weeks. The lines between PM, designer, and engineer are blurring as anyone can now create working demos to test hypotheses.
But that doesn’t mean the PRD has no purpose! Far from it.
The modern PRD serves a different function than its bloated predecessor. Instead of being a comprehensive specification document that tries to predict every edge case, today’s PRD is a strategic alignment tool that captures the “why” behind what you’re building.
The modern PRD is lighter, sharper, and example heavy. It focuses on clarity of thought rather than completeness of documentation.
5 steps to write a great PRD in the AI era:
1. Start with a hypothesis
The foundation of any good PRD is a clear problem statement and strategic rationale. Don’t just describe what you’re building — explain why it matters.
• What problem are we solving? Be specific about the user pain point or business opportunity
• What strategy does this support? Connect your feature to broader company goals and OKRs
This isn’t about creating a perfect requirements document. It’s about ensuring everyone understands the bet you’re making and why it’s worth making.
2. For AI features: Be obsessively specific about behavior
AI features are different from traditional features because they’re probabilistic, not deterministic. Your PRD needs to account for this reality.
• Include example prompts and expected outputs to show exactly how the AI should behave
• Define rejection criteria for when the AI should gracefully decline or ask for clarification
• Emphasize how it should handle edge cases and weird inputs because users will definitely find ways to break it
Think of this as training data for your team, not just the AI. Everyone needs to understand the intended behavior patterns.
3. Call out non-goals explicitly
What you don’t build is often more important than what you do build. Modern PRDs excel at creating boundaries.
• What isn’t included in this version? Be crystal clear about scope limitations
• What trade-offs are you making? Acknowledge what you’re sacrificing for speed or simplicity
This prevents scope creep and helps stakeholders understand your prioritization decisions. It also makes future iterations easier to plan.
4. Define the roll-out strategy
In the AI era, how you launch is as important as what you launch. Your PRD should address the deployment strategy upfront.
• A/B test or full launch? Define your testing approach and success metrics
• What are the passing criteria? Set specific guardrails and performance thresholds
• How will you monitor and iterate? Plan for rapid feedback cycles and improvements
This is especially crucial for AI features where performance can vary significantly across different user segments and use cases.
5. Use your PRD as a living document
The best PRDs in the AI age are conversation starters, not conversation enders.
• PRDs are for alignment, not dictatorships — they should facilitate discussion and decision-making
• Use it for discussion, not documentation — the goal is shared understanding, not comprehensive coverage
• Update it as you learn — your PRD should evolve as your prototype teaches you new things
Remember: in a build-first culture, your PRD works alongside your prototype, not instead of it.
The PRD isn’t dead. The bad PRD is.
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