What are Product Requirement Prompts (PRPs)?

If you've ever tried to explain your product vision to a developer, you know the challenge: translating what's in your head into something that can actually be built. Now imagine doing that with AI agents. That's where Product Requirement Prompts (PRPs) come in.

The Traditional Problem

Product Requirements Documents (PRDs) have been the standard for decades. They're comprehensive, detailed, and... often ignored. Why? Because they're written for humans who interpret, assume, and fill in gaps. AI doesn't work that way.

"AI agents need precision where humans need context. PRPs bridge that gap."

Here's what typically happens with traditional requirements:

• Ambiguity: "Make it user-friendly" means different things to different people
• Missing context: Assumptions about what's "obvious" lead to gaps
• Over-documentation: 50-page documents that no one reads completely
• Lost in translation: Technical implementation differs from business intent

What Makes PRPs Different

Unlike traditional PRDs, Product Requirement Prompts are:

1. Bite-sized

Each PRP focuses on a single feature or function. Instead of a monolithic document, you have modular, manageable pieces that can be built and tested independently.

2. Machine-readable

Written in a format AI tools can directly execute. No interpretation needed - the instructions are clear and unambiguous.

3. Context-complete

Include all necessary information without assumptions. Every PRP stands alone with all the context needed to build the feature.

4. Platform-agnostic

Work with any AI coding tool - Claude, GPT-4, or whatever comes next. The principles remain the same.

A Real Example

Let's say you want to build a user authentication system. Here's how a traditional requirement might look versus a PRP:

// Traditional Requirement:
"Users should be able to log in with email and password"

// Product Requirement Prompt:
"Create a secure user authentication system that:
- Accepts email (validated format: [email protected]) and password (min 8 chars, 1 uppercase, 1 number)
- Implements bcrypt hashing with salt rounds of 10 for password storage
- Returns JWT token valid for 24 hours with refresh token valid for 7 days
- Handles these error cases:
  - Invalid email format: 'Please enter a valid email address'
  - Wrong credentials: 'Invalid email or password'
  - Account locked: 'Account locked due to multiple failed attempts'
  - Server error: 'Something went wrong. Please try again.'
- Logs all login attempts with timestamp, IP address, and success/failure status
- Implements rate limiting: max 5 attempts per IP per 15 minutes
- Stores user sessions in Redis with 24-hour TTL
- Includes password reset functionality with secure token generation"

See the difference? The second one is so clear that an AI can build it without asking questions. Every edge case is considered, every requirement is specific.

The Anatomy of a Great PRP

Every effective PRP follows this structure:

1. Clear Objective

Start with what the feature should accomplish. Not how, but what.

2. Input Specifications

Define exactly what data comes in, including formats, validation rules, and constraints.

3. Processing Logic

Explain the business rules and transformations that need to happen.

4. Output Expectations

Specify what should be returned, in what format, and under what conditions.

5. Error Handling

List all possible error scenarios and how to handle each one.

6. Non-functional Requirements

Include performance expectations, security needs, and compliance requirements.

From Vision to Victory

The beauty of PRPs is that they democratize AI development. You don't need to understand machine learning or neural networks. You need to understand your business and be able to articulate what you want clearly.

At Monotasker, we've seen founders go from idea to working prototype in weeks, not months. The secret? They stopped trying to learn AI and started focusing on translating their vision into PRPs.

Getting Started with PRPs

Ready to write your first PRP? Here's a simple template:

Create a [FEATURE NAME] that:
- Accepts [INPUTS with specifications]
- Validates [VALIDATION RULES]
- Processes [BUSINESS LOGIC]
- Stores [DATA STORAGE requirements]
- Returns [OUTPUTS with format]
- Handles errors:
  - [ERROR 1]: [HOW TO HANDLE]
  - [ERROR 2]: [HOW TO HANDLE]
- Follows these constraints:
  - [CONSTRAINT 1]
  - [CONSTRAINT 2]

Common PRP Mistakes to Avoid

1. Being too vague: "Good UX" isn't specific enough
2. Missing edge cases: What happens when things go wrong?
3. Forgetting constraints: Rate limits, data privacy, compliance
4. Assuming context: Each PRP should be self-contained
5. Over-engineering: Start simple, iterate based on feedback

The Future of Product Development

PRPs represent a fundamental shift in how we build products. Instead of lengthy development cycles with constant back-and-forth, we can:

• Iterate faster: Build, test, and refine in hours instead of weeks
• Reduce miscommunication: Clear specifications leave no room for misinterpretation
• Empower domain experts: Your industry knowledge becomes your superpower
• Scale efficiently: Reuse and adapt PRPs across projects

Your Next Step

PRPs aren't just a new way to document requirements—they're a new way to think about building with AI. When you stop multitasking between vision and implementation details, you can focus on what matters: creating value for your users.

Want to master the art of writing PRPs? Our Prelude service helps you transform your idea into your first set of PRPs in just 24 hours. You'll leave with 5-10 PRPs ready to build your MVP.

Book your Prelude session and start building with clarity and confidence.