PRD Examples for AI Coding Agents

When you hand a product requirement document to a human colleague, they fill in the gaps. They know what "nice to have" means. They understand the company context. They can ask follow-up questions.

AI coding assistants cannot do any of that. They take your words literally, and when your words are vague, they guess. The results are often functional but architecturally wrong.

Here is a practical comparison showing how the same feature requirement translates into radically different AI outputs depending on specification quality.

The "Standard" Way (High Specification Debt)

This is how most product teams write PRDs. It feels natural because it mirrors how we communicate verbally. But for an AI agent, this document is a minefield of ambiguity.

# Audio Upload Feature

Last week, the product team was discussing the new audio upload feature. 
We really need the system to allow users to upload their files, organize 
them nicely, and play them back. It should also have some cool real-time 
frequency visualization like we saw on that competitor's site, and basic 
playlist management. Make sure it scales well because we expect a lot 
of traffic.

**Priority:** High
**Owner:** Product Team

Why This Fails

Let us be honest about what happens when you feed this into Cursor or Windsurf. The AI encounters "organise them nicely" and has no idea what that means. Does it want a folder structure? Tags? A search function? It guesses based on training data, which may or may not match your actual needs.

"Cool real-time frequency visualisation" is even worse. The AI might implement a simple waveform. It might build a full spectrogram. It might add three different visualisation modes you never asked for, because "cool" has no technical definition.

"Make sure it scales well" is perhaps the most dangerous phrase here. The AI might add caching layers, database sharding, and a CDN configuration for a feature that will see fifty users per month. Or it might build something that falls over at a hundred concurrent uploads. There is simply no way to know.

The 4ge Way (AI-Ready)

This is the structured output 4ge generates from your visual flows and feature canvas. It removes every ounce of ambiguity while remaining readable to humans.

# Audio Upload and Playback Feature Specification

## Objective
Implement an audio management system supporting upload, organisation, 
and playback with frequency visualisation.

## Phase 1: Storage and Schema

* **Requirement 1.1:** Implement AWS S3 bucket configuration for 
  audio file storage.
* **Requirement 1.2:** Define PostgreSQL schema for AudioTrack entity 
  including id, user_id, s3_url, duration, and created_at.

## Phase 2: Upload API

* **Requirement 2.1:** Create RESTful endpoint POST /api/v1/audio/upload.
* **Requirement 2.2:** Enforce file size limit of 50MB and restrict MIME 
  types to audio/mpeg and audio/wav.

## Phase 3: Playback and Visualisation

* **Requirement 3.1:** Implement HTML5 Web Audio API for playback 
  transport controls.
* **Requirement 3.2:** Utilise the Web Audio API AnalyserNode to render 
  a 64-band real-time frequency bar chart on an HTML canvas element.

## Constraints

* **Security:** Validate MIME types on both client and server side
* **Performance:** Generate waveform visualisation client-side only
* **Scale:** Initial architecture supports 1,000 concurrent streams

Why This Works Better for AI

The difference is stark. Notice how each requirement references specific technologies: AWS S3, PostgreSQL, HTML5 Web Audio API, AnalyserNode. The AI no longer needs to guess which database you prefer or how to implement visualisation. It simply follows instructions.

The phased approach is equally important. Rather than attempting to build everything in one monolithic prompt, you can ask the AI to implement Phase 1, review the output, then proceed to Phase 2. This maintains architectural control and prevents the "lost in the middle" phenomenon where AI agents forget early requirements by the time they reach later sections.

The constraints section is pure gold for AI agents. Instead of the meaningless phrase "scales well," you have a concrete target: 1,000 concurrent streams. Instead of worrying about performance, the AI knows to keep visualisation client-side.

The Token Economics

Here is something most developers do not consider. The narrative PRD contains roughly 85 words. The structured specification contains roughly 145 words. That is a 70% increase in length, but it reduces the total tokens consumed across your AI workflow by an order of magnitude.

How? Because the narrative version requires iteration. The AI builds something wrong. You explain the issue. It builds again. Still wrong. Another explanation. By the third or fourth attempt, you have consumed more tokens than the structured specification would have required in a single shot.

Related Examples

• Acceptance Criteria Examples for AI agents
• Development Brief Examples for token-optimised task breakdowns
• User Flow Documentation Examples for structured navigation specs