The Death of the PRD: Why Markdown Files Are the Future of Product Requirements

A new development paradigm where code and documentation are inseparable. They live, breathe, and evolve as one.

Welcome to the Document Graveyard

Every product team knows this story: A product manager spends days crafting the perfect PRD in Notion. Stakeholders review it. Design signs off. Engineering gives the thumbs up. Development begins. The product ships.

Then reality hits.

Users provide feedback. The market shifts. A better technical solution emerges. Features get cut, modified, or added. But who updates the PRD? Usually, no one. It sits there, a monument to what we thought we were building three months ago, while the actual product has evolved into something entirely different.

Welcome to the document graveyard, where good intentions go to die and where new team members go to get confused.

This isn’t just frustrating; it’s actively harmful. Outdated documentation leads to misaligned teams, confused stakeholders, and developers building the wrong things. We’ve all been there: six months into a project, nobody knows which doc is the source of truth anymore.

The Paradigm Shift: Code and Documentation Are One

The rise of agentic AI coding tools like Claude Code and Codex hasn’t just changed how we write code. It’s fundamentally changing how we should think about product documentation.

Here’s the crucial insight: We can’t think about code and documentation separately anymore.

These AI tools need context to be effective, and the most efficient way to provide that context? Markdown files that live directly in your codebase. But here’s the revolutionary part: If you’re already writing requirements in .md files to give AI the context it needs to build, why not make those files the single source of truth that evolves with your product?

The traditional separation between “documentation” and “implementation” is an artifact of the pre-AI era. When your AI engineer can read, understand, and update both simultaneously, keeping them separate becomes not just inefficient but absurd.

Understanding Claude Code’s File Structure

When using Claude Code, the tool looks for specific markdown files to understand your project:

The CLAUDE.md Convention

Claude Code automatically reads CLAUDE.md files when working in your directory, treating them as authoritative instructions for your project. The /init command can create this project file automatically, analyzing your codebase to summarize the project’s purpose, architecture, and key components.

The naming hierarchy works like this:

• CLAUDE.md: The standard file checked into git and shared across your team

• CLAUDE.local.md: Personal preferences that are gitignored and not shared

• Subdirectory CLAUDE.md files: Context specific to that part of the project (e.g., a /tests directory)

Claude Code searches for these files in the root of your repo (or wherever you run claude from), and in any parent directories. This is particularly useful for monorepos where you might have CLAUDE.md files at multiple levels.

The New Workflow: Living Requirements

Here’s how it actually works in practice:

1. Structure Requirements Like a Knowledge Tree

Create your product specs as .md files in a hierarchical folder structure that mirrors how humans actually think:

project/
├── CLAUDE.md                  # Top-level product vision & context
├── features/
│   ├── authentication/
│   │   ├── requirements.md   # Feature-specific details
│   │   └── oauth-flow.md     # Sub-feature specs
│   └── payments/
│       ├── requirements.md
│       └── stripe-integration.md

This structure leverages “cascading context.” Claude reads from specific to general, building understanding from the ground up. Claude Code can read CLAUDE.md files in subdirectories to provide context specific to that part of the project, creating a natural hierarchy of understanding.

2. Direct-to-Engineer Communication

When you need to make a change, you don’t update a PRD first and wait for approval. You go straight to your “engineer” (Claude) with the context window open and request the changes. The AI has all the context it needs from the .md files, understands the current state of the product, and can implement changes immediately.

This is where traditional Agile starts to feel prehistoric. No more sprint planning for a feature you’ll pivot on in three days. No more extensive documentation for an experiment that might not work. You think, you build, you learn. All in real-time. The feedback loop collapses from weeks to minutes.

3. Automatic Documentation Updates

Here’s the magic: Every time you confirm a change (make a commit), you can instruct Claude to update the relevant .md files with what changed. The documentation evolves with the code, staying perpetually current.

You can even create this as a persistent instruction in your CLAUDE.md file, telling Claude to “treat all CLAUDE.md files as living API documentation” and to “always check for relevant CLAUDE.md files and update them when changes impact their accuracy.”

Example of a living requirement file:

## Authentication Requirements
<!-- Last updated: 2024-11-09 by Claude Code -->

### Current Implementation
- OAuth 2.0 with PKCE flow ✅
- Biometric login for mobile ✅ (Added 2024-11-09)
- ~~Password-based auth~~ (Deprecated 2024-11-08)

### Architecture Notes
- Session management via JWT tokens
- Refresh token rotation implemented
- Rate limiting: 100 requests/minute per IP

4. Fresh Context for Every Feature

Creating a new Claude instance for each task provides better, more focused performance. Claude isn’t trying to juggle the entire product history but is laser-focused on the specific feature at hand. This approach naturally aligns with the .md file structure, where each feature can have its own focused requirements that Claude references.

Why This Changes Everything

The End of Staleness

Documentation can’t become stale if it’s updated with every commit. It’s not a separate task someone has to remember to do. It’s part of the development flow itself. When code and documentation are one, they evolve together or not at all.

The Death of “Documentation Debt”

We’ve all heard of technical debt, but documentation debt is just as real and often more painful. With this approach, there is no documentation debt because documentation updates are atomic with code changes. You literally cannot commit code without the option to update the docs.

Instant Onboarding

New team members (human or AI) can understand any part of the system by reading the relevant .md files. These files become living API documentation for your future self. They explain not just what the system should do, but what it actually does.

Fearless Experimentation

Want to try a completely different approach? Just build it. If it works, the documentation updates automatically. If it doesn’t, revert both code and docs in a single git operation. No wasted documentation effort on failed experiments. No meetings about updating specs for something that might not work.

Scaling Beyond Engineering

For teams that need to share product specs with non-technical stakeholders, the solution scales beautifully:

Automated Translation Pipeline

Set up GitHub Actions to trigger nightly builds that:

1. Digest all .md files from your repo

2. Use AI to transform technical specifications into audience-appropriate documentation

3. Push to Notion, Confluence, or wherever your business teams live

4. Maintain separate “views” for different audiences

The transformation can be intelligent:

From (in repo):

## Auth Requirements
- Implement OAuth2 with PKCE flow
- Redis session store with 24hr TTL
- Rate limit: 100 req/min per IP
```

**To (in Notion for sales team):**
```
✨ User Authentication
- Single sign-on for seamless access
- Secure 24-hour sessions
- Enterprise-grade security controls
[Visual diagram of login flow]

Sales gets their battle cards. Marketing gets feature descriptions. Support gets how-it-works guides. All automatically generated from the single source of truth that lives with your code.

Best Practices for Implementation

Keep It Concise

CLAUDE.md files consume part of your token budget with every interaction, so keep them concise while preserving critical information. Focus on documenting what IS, not what WAS. No changelogs in the main files.

Use Hierarchical Context

Every piece of information should be as context-efficient as possible. Subdirectory files matter just as much as the main one. Focus on project-specific patterns, architectural decisions, and common pitfalls specific to your codebase.

Living Documentation Mindset

Periodically review and refactor your CLAUDE.md files to keep them relevant. If you find yourself repeating the same instructions across multiple sessions, that’s a sign it belongs in your CLAUDE.md file.

The Post-PRD World

This isn’t a theoretical framework. It’s how modern product teams are already working. When you understand that .md files can be living, breathing product requirements, you unlock a new way of building:

• No separation between thinking and doing: Requirements and implementation evolve in lockstep

• No translation layer: What you write is what gets built

• No synchronization overhead: One source of truth, automatically maintained

• No documentation archaeology: The current state is always the documented state

The traditional PRD is dead because it was built for a world where documentation and implementation were separate activities, performed by different people, at different times. In a world where AI can turn requirements into code in seconds, and update documentation with every commit, that separation isn’t just inefficient. It’s absurd.

We can’t think about code and documentation separately anymore. They’re not two things that need to be kept in sync. They’re one thing that evolves together.

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Pete runs a 7-figure software agency in NYC, bringing products to market for Fortune 500s and startups. This approach has fundamentally changed how his teams deliver software: faster, more accurately, and with documentation that actually helps rather than hinders.