The PRP Framework Explained: Supercharge Your AI Coding Workflow in 10 Minutes

Hello everyone. Recently, the context engineering framework, PRP (Product Requirement Prompts), has gained significant exposure. PRPs are similar to traditional Product Requirements Documents (PRDs) but are specifically designed to instruct an AI coding assistant. This article will provide a comprehensive tour of the PRP framework and its repository.

Understanding Context Engineering

Before diving into the framework, it's crucial to grasp the principles of context engineering. For a deeper understanding, exploring resources on the topic is highly recommended. The core idea is to provide AI with high-quality, structured context to improve its performance, especially in complex coding tasks. This framework is a practical application of those principles.

What is the PRP Framework?

The PRP framework, or Product Requirements Prompts, is an evolution of the traditional Product Requirement Document (PRD). Drawing from years of experience in business analysis and product management, the framework was developed to enhance prompting and the context provided to AI coding assistants.

In short, a PRP combines a PRD with curated codebase intelligence and an agent runbook. The goal is to create the minimum viable packet of information an AI needs to plausibly ship production-ready code on the first attempt. It’s a planning framework for curating context, creating an implementation plan, and referencing documentation both within and outside your codebase.

A key driver for its creation was the need to work effectively on existing, mature codebases. The framework has gone through several iterations since its inception in the summer of 2024.

A Deep Dive into the Repository

The repository contains detailed instructions in the readme file to get you started. Let's explore its main components.

If you open the PRPs directory, you'll find another readme file. This one is specifically for the AI to consume when a PRP is created. The templates directory holds all the PRP prompt templates that the AI coding assistant uses to generate the final output.

The commands directory is where the action starts. It contains numerous commands that are generalized for broad use but are highly adaptable for specific codebases. Frequent-use commands include review-stage and review-general, which are invaluable for code reviews. There are also several development helpers for Git and other miscellaneous tasks.

The Core: PRP Commands and Use Cases

The PRP directory contains multiple versions of the framework, each tailored to a different use case. We will use PRP-base as our primary example.

The PRP-base command takes your feature idea as an argument. It’s crucial to invest time upfront in preparing a detailed feature request to feed into the PRP. The more effort you put in here—using sources like a Jira task, an epic, or a traditional PRD—the better the outcome.

The command instructs the AI to generate a complete PRP for a general feature implementation, conducting thorough research and ensuring all necessary context is passed to the AI to enable self-validation and iterative refinement.

The Research Process

The AI follows a structured research process: 1. Codebase Analysis: It delves into your codebase to gather all relevant context. 2. External Research: It searches online for libraries, implementation examples from GitHub or blogs, best practices, and common pitfalls. 3. Pattern Matching: It looks for similar patterns and features that already exist in your codebase. 4. Clarification: If the AI determines it lacks sufficient context from your initial argument, it will ask clarifying questions to fill the gaps.

PRP Generation

Once the research is complete, the AI uses the PRP-base template to generate the final document. The process emphasizes providing comprehensive context, including documentation, code examples, gotchas, design patterns, and a detailed implementation blueprint with pseudo-code and references to real files.

The Power of Validation Gating

A critical component of the PRP framework is validation gating. The AI is designed to perform syntax and style checks, as well as run unit tests. It operates in a loop, refining the code until all checks and tests pass. The framework supports various validation techniques:

• Linters and Unit Tests: These are the two fundamental validation methods. You can configure custom rules in your linters to enforce specific standards.
• Integration Testing: For APIs, you can use curl to perform integration tests, as the AI can execute its own bash commands.
• E2E Testing: When building front-end applications, you can use tools like Puppeteer or Playwright. By adding a validation level, you can instruct the AI to use these tools to test end-to-end user flows, clicking buttons and reading console output to ensure everything works as expected.
• MCP Servers: For tests involving deployments or Docker, you can use MCP (mock cloud provider) servers. For instance, with Digital Ocean, you can set up a validation level where the AI checks the implementation through the MCP servers and fixes it until it works.

The process concludes with a final validation checklist, ensuring all tests pass, there are no linting or type errors, and manual tests are successful.

Anatomy of a PRP Template

Let's examine the PRP-base template that the AI fills out.

This predefined template is populated with all the context gathered during the research phase. It includes front matter explaining the purpose and core principles to the AI, referencing the main readme file.

The template is structured around three core goals: 1. The Value: Why are you building this? 2. The Scope: What exactly are you building? 3. Success Criteria: How do you measure success?

The more context you provide related to these three areas in your initial argument, the better the result. If you provide minimal input, the AI will make assumptions, which may not align with your goals. It is critical to spend time on planning and preparation.

Other key sections of the template include: * Context: All the gathered context the AI will need for implementation. * Tree Structure: The desired codebase structure, often showing a "before" and "after" view. * Known Gotchas: A list of potential pitfalls. * Implementation Blueprint: This includes data models and structures. It's essential to review this section to ensure the architecture, data structures, and API contracts align with your project's standards. * List of Tasks: A detailed list of tasks to be performed. The framework uses information-dense keywords like find, inject, preserve, modify, and mirror to keep task descriptions concise. Pseudo-code is added for more complex tasks. * Integration Points: This section outlines where the new code will connect with the existing system. Careful review is needed here, as changes can range from minor tweaks to altering entire routes or migration files.

A Practical Example: Refactoring a Monolithic Codebase

An example run of a PRP involved refactoring a monolithic codebase with two files, each containing thousands of lines of code, into a well-structured vertical slice architecture.

The initial argument was a simple directive to refactor the codebase. The AI generated a comprehensive PRP, including: * Goal: To refactor the monolithic codebase into a vertical slice architecture, explaining why this was beneficial. * Success Criteria: Pulled from the project's claude.md file. * Context: The AI identified the need for specific library documentation (in this case, for uv to replace pip) and included it directly. It also referenced the claude.md for coding standards. * Tree Structure: It provided a complete "before" and "after" directory structure, showing the extensive modularization.

The autonomous run to execute this PRP took approximately 1 hour and 40 minutes, generating over 300 tests. The result was a nearly perfect, out-of-the-box solution that required only minor iteration to fix some import errors.

Executing the PRP

Once the PRP is generated and you have reviewed and approved the plan, you run the PRP-base-execute command.

/prp base-execute prps/your-generated-prp-file.md

The execution process follows a chain-of-thought sequence: 1. Load the PRP: The AI loads the file and gathers all the context. 2. Plan: It creates a comprehensive plan, breaking down the work into smaller to-do items. 3. Execute: It carries out the plan. 4. Validate: It runs the validation loops. 5. Complete: It finalizes the task, referencing the PRP throughout the process.

Note: It's a best practice to run the creation and execution of a PRP in separate, clean instances of your AI coding assistant. This prevents the AI from making assumptions based on the research phase, which can lead to hallucinations.

Handling Different Task Sizes

The PRP-base command is intended for large pieces of work, from user stories to entire features. For smaller tasks like bug fixes or minor updates, using the full PRP-base is overkill.

For these scenarios, the framework provides the PRP-task command. It's a streamlined version of the base PRP, using a similar process but tailored for smaller scopes. It will take a Jira ticket or GitHub issue and enrich it with a full implementation plan, documenting all the necessary steps.

From Idea to PRD: The Planning Command

The framework also includes a PRP-planning command, which essentially acts as a PRD generator. You can provide a feature idea, and it will create a first draft of a PRD based on a comprehensive template.

The generated PRD includes: * Executive Summary * Problem Statement & Solution Overview * Success Metrics & User Stories * Primary User Flows (with Mermaid graphs) * System Architecture (with Mermaid graphs) * Technical Specifications (with sequence diagrams) * API Contracts & Data Flow * Implementation Strategy & Priorities

This is an incredibly powerful tool for research and planning. You can use it to generate multiple reports and consolidate them into a final, validated PRD.

The Importance of the claude.md File

The repository includes a library of claude.md files for different frameworks like Astro, Python, and Next.js. These files contain project-specific instructions, patterns, and architectural guidelines.

As your project matures, your claude.md file should mature with it. Adding project-specific instructions will lead to exponentially better results from your AI assistant. A well-structured, modular, and easy-to-read codebase allows the AI to perform exceptionally well. If the codebase is a mess, the AI's output will reflect that.

Final Thoughts

The PRP framework is a powerful tool for leveraging AI in software development, especially on large, existing codebases. By providing structured, high-quality context, you can guide AI assistants to produce reliable, production-ready code. The framework is continuously evolving, with new features and improvements being added regularly. If you're interested in agentic engineering and getting the most out of your AI coding tools, exploring this framework is a worthwhile investment.