How to Write a PRD and Let AI Build It

The blueprint writes itself. Then builds itself.

A Product Requirements Document -- PRD -- is the blueprint for what you're building. In the old world, product managers spent weeks writing these. In the vibe coding world, AI writes them in minutes.

And then AI builds them too.

The Two-Step Process

Step 1: Describe your idea to an AI like Claude or ChatGPT. Ask it to produce a PRD. Be specific about what the thing should do, who it's for, and what the first version needs.

Step 2: Take that PRD and paste it into V0, Lovable, or your Cursor project. The building platform reads the requirements and generates working software.

That's it. Idea to blueprint to working app, often within an hour.

Idea in. Blueprint out. App shipped.

What Makes a Good PRD Prompt for AI?

The quality of your PRD determines the quality of your build. Here's what to include:

• What it does: "A streaming radio station with a bubble interface"
• Who it's for: "Music discovery enthusiasts"
• Core interactions: "Single click samples a track, double click opens a playlist"
• Technical preferences: "Use React, deploy to Vercel"
• Future considerations: "Should support recommendation engines later"

Live Example

During a Vibe Jam session, we built an online radio station called RadioBubble. The process:

1. Described the concept to Claude
2. Asked for a PRD
3. Simplified the PRD when the first version was too complex
4. Pasted the simplified PRD into V0
5. Published a working version within the session

The AI even suggested the system architecture -- event collectors, recommendation algorithms, and deployment strategies -- all from a conversational description.

When to Iterate on the PRD

If the first build isn't right, don't fight with the code. Go back to the PRD. Refine your description. Add specifics where the AI guessed wrong. Then rebuild.

Iterate at the highest level possible.

The PRD is cheaper to edit than the code. Always iterate at the highest level possible. This is agile backwards in practice — the user builds first, then refines.