Mastering AI Development with the PRP Framework |

Context engineering is the new frontier in AI-assisted coding. While the concept isn't entirely new, developers are now recognizing that investing time upfront to provide comprehensive context to AI assistants is what separates genuinely effective results from mere "vibe coding" or basic prompt engineering. Most developers are still navigating these ideas without a structured, systematic process.

This article dives deep into using context engineering for real-world production builds, expanding on a foundational template to offer a more advanced guide.

Why Context Engineering Matters

We've learned that vibe coding, while great for rapid prototyping, often creates systems that falter at scale. Prompt engineering, on the other hand, focuses on tweaking words and phrases to elicit slightly better single outputs from LLMs.

A more powerful way to think about it is that context engineering is a superset of prompt engineering. It’s not just about the prompt; it's about providing all the necessary information—examples, best practices, constraints, and architectural patterns—to an AI coding assistant from the start. This initial time investment can 10x your development process for building almost anything.

Introducing the PRP Framework

Numerous strategies for context engineering exist, but one that consistently proves effective is the PRP (Product Requirement Prompt) framework. It’s a genius system for structuring AI development.

The PRP framework is a systematic approach that combines a Product Requirement Document (PRD), curated codebase intelligence, and an agent runbook. It aims to create the minimum viable packet of information an AI needs to plausibly ship production-ready code on the first attempt. This framework has been developed over more than a year, evolving through many forms and inspired by concepts from the dev-dan and client communities.

A Practical Use Case: Building MCP Servers

To demonstrate the power of this framework, we have adapted a generic PRP template to be specific to building MCP (Multi-Capability Prompt) servers. This allows you to use the PRP framework to build any production-ready MCP server you need.

Let's explore how this works. In a standard context engineering repository, you might find a generic template. For instance, the slash commands would be universal, and the PRP template itself would apply to any kind of software.

/slash-commands
  - generic-command-1.md
  - generic-command-2.md
/prps
  - template.md

However, for a more targeted approach, we've created a specialized template for MCP servers. The slash commands are now specific to this task, and more importantly, the base PRP—the reference used to generate the full PRP—is tailored for building MCPs.

This hyper-tuned template serves as a powerful launching pad, ensuring much better results because the context is already aligned with the goal of building an MCP server.

The process to build your own MCP is straightforward:

Define Your Goal: Open the initial.md file and describe the MCP you want to create.
Generate the PRP: Use a simple command or prompt to generate your PRP based on your definition.
Validate: This is a critical step. Review the generated PRP to ensure it aligns with your vision. Don't blindly trust the AI.
Execute: Run the PRP to build your MCP server.
Iterate: Refine and build upon the generated code.

This entire process revolves around the power of the specialized MCP PRP-based template. And this is just the beginning. The vision is to build a massive repository of these use cases, so no matter the programming language or application type, a hyper-tuned template is available.

A Live Demonstration: The PRP Taskmaster

To showcase this, we built a non-trivial MCP server called the "PRP Taskmaster" in just two shots. After one follow-up prompt to fix a couple of bugs, we had over 15+ perfectly working tools.

The PRP Taskmaster is an MCP that takes a PRP, extracts tasks from it, and helps you manage them. It's conceptually similar to the well-known Claude Taskmaster but designed for PRPs instead of PRDs.

To test it, we used a pre-crafted prompt designed to validate all the server's functionality in a logical flow—starting a project, adding tasks, managing dependencies, and creating documentation.

Here's a simplified version of the test prompt:

"We've got this new MCP, the PRP Taskmaster. I want you to test all its functionality with a flow that makes sense for starting a new project. Create a sample PRP, add tasks and dependencies, and generate documentation. Keep it a simple demonstration."

The backend database was set up with tables for projects, tasks, and task dependencies.

-- Example Database Schema
CREATE TABLE projects (id SERIAL PRIMARY KEY, name VARCHAR(255));
CREATE TABLE tasks (id SERIAL PRIMARY KEY, project_id INT, description TEXT);
CREATE TABLE task_dependencies (task_id INT, depends_on_id INT);

The test began by mocking a PRP and sending it to the server to extract tasks. The project was successfully created and verified in the database. The process continued, listing projects, adding tasks, and running through a mock workflow of managing the project.

This demonstrates a crucial principle: always validate the output. When using a framework like PRP to code with AI assistance, it's vital to test every single tool and understand the code the AI has produced. Have the AI assistant create tests for you as part of the process.

The Philosophy Behind the PRP Framework

The core idea is that "garbage in, garbage out" applies doubly to prompt engineering. While prompt engineering is a part of the PRP framework, the framework itself is a superset that encompasses much more.

Global Rules vs. PRP Context

A key distinction in this workflow is what goes into your global rules versus what belongs in the PRP.

Global Rules (claude.md or similar): These are for constant, rarely changing rules that apply across your entire codebase. Think of them as foundational principles: naming standards, core architectural patterns, or utility functions that will be true forever. You can even have folder-specific rule files for different modules.
Slash Commands: These are drop-in workflows for planning or execution. For example, a create command is a generic research and planning workflow that becomes specific only when you pass in your plan. An execute command simply tells the AI how to process your PRP.
The Base PRP: This is where you put the specific context for the piece of work you are doing right now.

This separation ensures that your foundational principles remain consistent while allowing for unique context for each new feature or project.

Using the PRP Framework on Existing Codebases

The PRP framework was purpose-built for working on existing codebases. Its effectiveness has grown immensely with the improvement of AI models. With modern models, it's possible to reliably execute PRPs that are hundreds or even over a thousand lines long. While not perfect, the results are incredibly close to what's needed for production work.

Why Invest the Time in Context Engineering?

For those hesitant to invest this much time in "sharpening the axe," the rationale is clear. This is the same deep work that product managers and senior developers do for human teams. It forces you to answer critical questions:

Why are we building this?
Who are we building it for?
What should the user experience be?

When you work on an existing codebase, you already have a wealth of context from user feedback and established patterns. The PRP framework leverages this, providing the AI with a strong foundation to follow, which dramatically increases the reliability of the output. It would be nearly impossible to one-shot a large piece of work without such a solid foundation.

Step-by-Step: Building an MCP Server from Scratch

Let's walk through the three-step process using the template.

Step 1: Create `initial.md`

First, you describe the MCP server you want in the prps/initial.md file. This file should detail the features, examples, documentation references, and other considerations.

For our PRP Taskmaster, the initial.md specified: - Goal: Build a simple version of Taskmaster that parses PRPs instead of PRDs. - Features: A list of all the tools needed for the MCP. - Documentation: A reference to the original Claude Taskmaster GitHub repository, so the AI could study its implementation. - Considerations: Notes on potential gotchas, like how to handle environment variables.

Step 2: Generate and Validate the PRP

With the initial.md defined, you run a command to generate the PRP.

/PRP MCPre ./prps/initial.md

This process can take 10-15 minutes as the AI performs extensive research and planning. The result is a custom, lengthy PRP file.

Validation is essential. You must review this file. For example, during our build, the generated PRP included a step to update dev.vars, which is Cloudflare's equivalent of a .env file. You typically don't want an AI editing secrets directly, so that section was removed. This is why validating the PRP is a non-negotiable step.

The PRP also outlines the current and desired file structure, giving the AI a clear blueprint to follow.

Step 3: Execute the PRP

After validating and tweaking the PRP, you clear the context and execute it.

/PRP MCP execute ./prps/PRP-parser-mc.md

The AI will first analyze the PRP, create a comprehensive to-do list from all the documentation and references, and then begin executing the tasks one by one. It implements "validation gates," such as running a linter and creating and running unit tests, iterating until the server is complete and functional.

After about 25 minutes, the process completes, yielding a fully implemented MCP server with all tools built and checked. While it may not always work in a single shot, our example was production-ready after just one minor iteration to fix a couple of bugs.

Finally, you can run the server locally using a tool like the Cloudflare CLI.

wrangler dev --config wrangler.prp-taskmaster.toml

This simulates the production environment, and you can connect your tools to the local worker URL to see your new MCP in action.

Conclusion

The PRP framework is a powerful method for leveraging context engineering to 10x your development workflow. By providing structured, comprehensive context, you can guide AI assistants to build robust, production-ready applications with remarkable efficiency. The future of AI coding lies in building out these repositories of specialized templates, enabling developers to get started on any project with a massive head start.