The Concept of Context Engineering: Building Production-Ready AI Code

Context engineering is the new big thing for AI coding. As AI coding assistants rapidly evolve, the way we interact with them must evolve too. While the earliest approaches were all about “prompt engineering”—focusing on carefully-worded, one-off prompts to coax better responses—today’s best practitioners are shifting to a new paradigm: context engineering. This isn’t just a buzzword or rebrand. It’s a transformative approach to turning AI from a prototyping tool into a true production-grade coding partner.

What is Context Engineering?

At its core, context engineering is the systematic practice of providing extensive, structured, and relevant context to AI coding assistants before task execution. This means you front-load all the information, examples, best practices, architectural constraints, and references the AI will need—enabling it to generate robust, scalable, and production-ready output from the start.

Or, as I like to say:

Context engineering is a superset of prompt engineering. It's a part of it, but context engineering is so much more.

Where prompt engineering is about iteratively tweaking phrasing to get a single, better output, context engineering is about investing upfront in the structure and depth of the information you give the model, so it can deliver consistently good, maintainable code—not just once, but every time.

Prompt Engineering vs. Context Engineering vs. Vibe Coding

• Vibe Coding: Rapid prototyping with minimal structure—“just vibe it out and see what the AI gives.” Fast for demos, but brittle and unscalable. Vibe coding builds prototypes that break when you try to scale.
• Prompt Engineering: Focuses on crafting and refining single-shot prompts. Useful for isolated outputs, but often lacks repeatability or depth for complex systems.
• Context Engineering: Encompasses prompt engineering, but also incorporates codebase context, best practices, documentation, examples, constraints, and more—all structured upfront. This is how you bridge the gap from prototype to production.

Why Context Engineering Matters (Especially Now)

Until recently, the limitations of AI models—especially their small “context windows”—meant you couldn’t reliably provide enough information for them to work on large or complex systems. That’s changed. With models like Claude 4, GPT-4, and their peers, you can give 1,000-1,500 lines (or more) of context, and expect reliable, coherent outputs.

The effectiveness of this approach has significantly increased with newer AI models capable of handling large context windows (1000-1500 lines) reliably.

This opens the door to serious codebase refactoring, multi-file implementations, and even greenfield projects—if you invest in engineering the right context upfront.

From Prototype to Production: The Context Engineering Advantage

The harsh truth of AI coding is summed up in a timeless industry mantra:

The industry mantra, garbage in, garbage out applies doubly to prompt engineering.

If your input is shallow or unstructured, even the best models can only deliver “vibe code.” But when you carefully curate and structure your context, you unlock what I call:

This, my friend, is Peak AI coding.

Key Insight: To graduate from prototypes to production-grade code using AI, the key is to invest significant time in preparing detailed context before execution, rather than reactively tweaking prompts.

How Context Engineering Works: Practical Frameworks

There are many ways to approach context engineering, but one framework that’s been transformative for my workflow (and hundreds of others) is the PRP Framework (Product Requirement Prompt), developed by Raasmus.

• PRP = PRD (Product Requirements Doc) + Curated Codebase Intelligence + Agent Runbook
• The PRP aims to be the minimum viable packet an AI needs to plausibly ship production-ready code on the first pass.
• This includes the business requirements, codebase context, best practices, architectural patterns, and a “runbook” for execution.

Think of the PRP as the AI coding assistant’s onboarding manual and project spec, all in one. Examples, references, and explicit constraints are all included before you ask the AI to generate or change code.

Step-by-Step Example: Building a Production-Ready MCP Server

To illustrate, let’s walk through a practical scenario: building a Modular Command Processor (MCP) server using the PRP framework and context engineering best practices.

1. Define Your Project (initial.md):
   • Describe the features, tools, and business logic you need.
   • Reference example implementations and external documentation.
   • List out specific requirements and any known “gotchas.”
2. Generate the PRP:
   • Use an AI assistant (like Claude Code or Gemini CLI) with a specialized PRP template.
   • Let the AI process your plan, pull in relevant context, and produce a comprehensive PRP tailored to your project.
3. Validate the PRP:
   • Review the generated PRP carefully—does it reference the right files, tools, and business rules?
   • Remove or adjust anything risky (e.g., don’t let the AI edit secrets directly).
   • Emphasize validation gates—unit tests, linting, and documentation references.
4. Execute and Iterate:
   • Clear the AI context and use your PRP as input for code generation.
   • The AI will analyze, plan, and implement code according to the PRP.
   • Validate outputs, run tests, and fix issues with additional iterations as needed.

In my latest project, this approach let us build a non-trivial MCP server with 18 tools in just two AI-driven passes—fixing only minor bugs manually. That’s the power of context engineering in action.

Actionable Tips for Effective Context Engineering

• Invest time upfront. Don’t rush the planning or context gathering phase. The more precise your input, the better your output.
• Leverage templates and frameworks. Use PRP templates or build your own; they provide reusable, structured starting points.
• Be explicit about constraints and best practices. Don’t assume the AI “knows” your standards—spell them out.
• Reference existing codebases and patterns. Give the AI as much architectural guidance as possible.
• Validate everything. Carefully review generated plans and outputs, especially before running code in parallel or at scale.
• Iterate and refine. Use the AI as a partner in planning and review, not just code generation.

Context Engineering in the Real World: Beyond the Prototype

The difference between “vibe coding” and production-grade AI coding can’t be overstated. Where vibe coding is fun for hackathons and quick demos, it leaves you with fragile, unscalable code. Context engineering, by contrast, is how you:

• Ship reliable, maintainable apps
• Onboard new developers (or AIs!) quickly
• Scale projects safely and systematically
• Accelerate delivery by reducing back-and-forth and rework

As AI models continue to improve and context windows grow ever larger, those who master context engineering will be poised to lead the next era of software development.

Conclusion: The Future is Structured

Context engineering isn’t just a trend—it’s a paradigm shift. It’s about treating your AI assistant like a real developer: you wouldn’t hire a coder, drop them into a project with no documentation or planning, and expect miracles. The same goes for AI.

By investing in structured, comprehensive context upfront, you can 10x your workflow and build systems that scale. And as templates and community frameworks (like PRP) proliferate, getting started is easier than ever.

Context engineering is the new big thing for AI coding.

Embrace it, and you’ll find yourself on the cutting edge of “Peak AI coding.”

Want to learn more or get started with ready-to-use templates? Check out the Dynamis Context Engineering Repository and join the community advancing this crucial field.

The PRP (Product Requirement Prompt) Framework: Systematizing Context Engineering for AI Coding

In today’s world of AI-powered coding, context engineering has quickly emerged as the game-changer that separates robust, production-ready builds from fragile prototypes and “vibe coding.” But while the idea of providing extensive context to AI is gaining traction, most practitioners still lack a systematic methodology. Enter the PRP (Product Requirement Prompt) framework—a comprehensive approach to context engineering developed by Raasmus and refined through real-world use and iteration.

Why Context Engineering Matters

Before we dive into the PRP framework, it’s important to understand the landscape. Traditional prompt engineering focuses on tweaking wording for better LLM outputs, but often falls short for complex or production-grade projects. As Raasmus and others have observed, “context engineering is a superset of prompt engineering.” It’s about providing all the necessary information, examples, constraints, and best practices up front—an investment that, while significant, can easily 10x the effectiveness of AI-assisted software development.

Introducing the PRP Framework

The Product Requirement Prompt (PRP) framework is Raasmus’s answer to the need for structure in context engineering. Developed over more than a year during the creation of a real-world “valuation engine,” the PRP framework was born out of necessity, not theory. Its goal is ambitious yet pragmatic:

"A PRP is a PRD plus curated codebase intelligence plus agent runbook and it's aiming to be the minimum viable packet an AI needs to plausibly ship production ready code on the first pass."

In other words, a PRP is much more than a prompt—it’s the minimum viable packet of context an AI needs to generate code that can go straight to production, even on the first attempt.

Dissecting the PRP Structure

The PRP framework synthesizes three core components:

• Product Requirement Document (PRD): The high-level feature or product specifications, user stories, acceptance criteria, and business logic.
• Curated Codebase Intelligence: Key information about the current codebase, including architecture, patterns, standards, and relevant files or modules.
• Agent Runbook: Step-by-step instructions and workflows for the AI assistant, outlining how to implement the feature, validate its work, and adhere to project conventions.

This triad ensures that the AI receives not just the “what” and “why,” but also the “how” and “where,” mapping the pathway from the current state of the codebase to the desired end result.

Story of Origin: From Need to Framework

The PRP framework wasn’t designed in a vacuum. As Raasmus recounts, it originated from a very practical challenge:

“I needed it for an existing project that I was building at the time, a valuation engine. So yeah, I used PRP—or the very baby version of PRP—to build that out and ship it to production.”

This foundation in real-world engineering makes the PRP framework especially robust for existing codebases—not just greenfield projects. In fact, Raasmus emphasizes:

"It is purposely built for working on existing codebases. That's the use case I needed it for when I started building it."

Organizing Context: The PRP and Global Rules

For optimal clarity and maintainability, the PRP framework advocates separating context into two layers:

• Global Rules File (e.g., claude.md): For constant, unchanging standards, naming conventions, architectural patterns, and principles that apply across the codebase or organization.
  “I treat my cloud MD as where I put like the constant rules that will very rarely change.”
• The PRP: For all specific context related to the feature or task at hand, including unique acceptance criteria, dependencies, and implementation details.

This separation means you maintain a clean, evolving repository of standards while empowering each PRP to focus sharply on the immediate task.

How to Build and Use a PRP: Practical Process

1. Describe Your Feature in initial.md:
   • Document the feature or change you want, including business logic, user flows, and desired tools.
   • Reference related examples, documentation, or repositories for richer context.
   • List known “gotchas” or edge cases for the AI to consider.
2. Generate the PRP:
   • Use your AI assistant (e.g., Claude, Gemini CLI, Cursor) to synthesize a PRP from your initial.md and any base templates.
   • Ensure the PRP includes sections for both the current codebase structure and the desired final structure.
3. Validate the PRP:
   • Carefully review the generated PRP. Check for accuracy, completeness, and alignment with business goals.
   • Edit out any instructions that might create security risks or break conventions (e.g., avoid giving AI access to sensitive secrets).
4. Execute and Iterate:
   • Run the PRP through your AI coding assistant. Monitor as it generates code, documentation, and tests.
   • Validate outputs rigorously. Run tests, check for bugs, and iterate as needed.

Actionable Tip: When using the PRP framework with other AI tools (like Gemini CLI or Cursor), simply use the PRP commands and templates as regular prompts—no need for special slash command support.

Real-World Example: Building an MCP Server

The power of the PRP framework shines in practical applications. In developing a Taskmaster MCP server (a multi-tool project management application), the process looked like this:

• Created a detailed initial.md outlining desired features (task extraction, GitHub OAuth, Cloudflare deployment, etc.) and referenced the Claude Taskmaster repository for inspiration.
• Generated a custom PRP based on a base template tailored for MCP servers.
• Validated and tweaked the PRP to ensure it matched requirements and followed security best practices.
• Ran the execute command, allowing the AI to scaffold the entire MCP server, implement tools, create documentation, and set up tests—all in “two shots” (only one minor follow-up fix needed).

The result? Eighteen fully-functional tools in a non-trivial production-ready MCP server, with green checkmarks across testing and deployment.

“A PRP is defined as the ‘minimum viable packet an AI needs to plausibly ship production ready code on the first pass.’”

Best Practices and Lessons Learned

• Validation is critical: Don’t blindly trust AI outputs. Always review, test, and adjust as needed.
• Use modular PRPs: For especially complex features, consider creating a separate PRP per tool or module.
• Leverage templates: Base PRP templates for common project types (like MCP servers) accelerate onboarding and reduce cognitive load.
• Document both current and target state: Mapping both the starting point and the end goal gives the AI a clear pathway for implementation.
• Regularly update global rules: As your codebase evolves, keep claude.md and similar files up to date.
• Adapt for your toolchain: The PRP methodology is tool-agnostic—commands and principles work with any AI coding assistant able to process rich prompts.

Why Invest in Context Engineering?

Some may wonder—is it worth putting so much time into context engineering? Raasmus offers a clear perspective:

“For me, it's obvious because I come from product management. I used to do this type of work anyways but not for an AI assistant but for a team of developers. Someone building a real project is doing this work, whether it’s you, an AI, or someone else. Thinking deeply about the important questions—why are we building this, who is it for, how should the user experience look—always pays off.”

In short, context engineering is the “axe sharpening” of AI coding. It front-loads the thinking, reducing rework and massively accelerating reliable delivery.

The Future: Template Repositories and Community

The PRP framework is just the beginning. The Dynamis community and others are actively building repositories of PRP templates for different languages and application types, making it easier than ever to launch new projects with production-readiness from the outset.

Key Takeaways

• The PRP framework systematizes context engineering, combining PRD, codebase intelligence, and agent runbooks.
• Its goal is to deliver the “minimum viable packet” for AI to ship production-ready code, even on first attempt.
• It excels on both new and existing codebases, driving reliability, maintainability, and speed.
• Separation of global rules from feature-specific context keeps projects organized and scalable.
• Thorough validation and iteration are essential to success.

Memorable Quote

"A PRP is a PRD plus curated codebase intelligence plus agent runbook and it's aiming to be the minimum viable packet an AI needs to plausibly ship production ready code on the first pass."

Ready to take your AI coding to the next level? Start building with the PRP framework, and experience for yourself how disciplined context engineering can transform your workflow, whether you’re shipping MVPs or maintaining mission-critical systems.

Mastering Practical AI Coding: Applying the PRP Framework for Production-Ready Builds

AI-assisted coding is entering a transformative era. With the advent of context engineering, the days of “vibe coding” and basic prompt tweaks are giving way to systematic, production-hardened workflows. At the heart of this evolution is the PRP framework—a method that turns AI from an unpredictable coder into a reliable, scalable development partner. In this piece, I’ll walk you through the practical application and workflow of the PRP methodology, with a focus on building MCP servers using specialized templates. Whether you’re an AI coding enthusiast or a team lead seeking efficiency, these insights will help you boost output, quality, and confidence in your AI-driven projects.

What is the PRP Framework?

PRP stands for Product Requirement Prompt, a concept developed by Raasmus after years of technical product management. At its core, a PRP is:

• A PRD (Product Requirements Document)
• Curated codebase intelligence
• An agent runbook

The aim? To provide the minimum viable packet of context an AI assistant needs to plausibly ship production-ready code on the first pass.

As Raasmus puts it, “A PRP is a PRD plus curated codebase intelligence plus agent runbook, aiming to be the minimum viable packet an AI needs to plausibly ship production-ready code on the first pass.”

Why Context Engineering Trumps Prompt Engineering

Prompt engineering—carefully wording your AI requests—is valuable, but limited. Context engineering is the superset: it’s about supplying the AI with all relevant context, examples, best practices, constraints, and prior art. Yes, it’s a time investment, but as I’ve seen firsthand: man, will it 10x your process for building literally anything.

"Don't trust the AI blindly. But then you can execute your PRP and this is going to build your MCP server."

Templating: The Launchpad for Efficiency

A breakthrough in the PRP methodology is the use of pre-engineered templates for specific use cases—think of them as reusable blueprints. Recently, Raasmus and I co-created a PRP template tailored for building production-ready MCP (Multi-tool Claude-compatible Provider) servers with Cloudflare and TypeScript. This isn’t some generic skeleton; it encapsulates nuanced requirements, reference patterns, and codebase scaffolding specific to MCP servers.

• Efficiency: No need to reinvent the wheel. Templates serve as a “launching pad,” drastically reducing manual effort.
• Specialization: Each template is hyper-tuned for its case—be it a different language or application.
• Scalability: The Dynamis community is building a massive repository of these, covering diverse stacks and use cases.

For example, using our MCP server template, I was able to build a complex “PRP Taskmaster” server—complete with 18 tools—in just two iterative “shots” (one round to fix minor bugs). That level of speed and reliability is game-changing.

The Three-Step PRP Workflow (With Practical Insights)

1. Define High-Level Requirements (initial.md):
   Write down, as specifically as possible, what you want to build. List features, business logic, gotchas, and examples. This becomes the seed for your PRP.
2. Generate the PRP:
   Use your AI assistant (e.g., Claude, Gemini CLI, Cursor, etc.) to expand initial.md into a full PRP by invoking the relevant slash command or prompt chain.
   • The pre-built template injects proven context, references, and structure.
   • The AI will pull in additional context as needed for your specific goals.
3. Manual Validation, Then Execution:
   
   • Never blindly trust the AI. Read through the PRP. Validate for accuracy, completeness, and alignment with your intentions.
   • Adjust as needed—remove dangerous actions (like direct secret edits), clarify instructions, or add missing details.
   • Once validated, execute the PRP: the AI will scaffold, implement, and test the codebase according to the plan.

This methodology is robust enough to work on existing codebases too—just tailor your PRP to reference the relevant modules and desired changes.

Validation: The Non-Negotiable Principle

Perhaps the single most important mindset shift is this: Validation is not optional. Whether reviewing the generated PRP or the final code, you must be actively involved in the process. As Raasmus emphasizes:

"Read through your PRPs before you run the execute command, which is the last step."

Blind trust in the AI is what leads to “vibe coding” disasters—bugs, misaligned features, and security holes. Instead, treat the AI’s output like a junior developer’s work: review, test, and iterate.

• AI can even test its own work: prompt it to run through the new app’s functionalities in a logical user flow, as I did with the “PRP Taskmaster” server.
• Use validation gates: have the AI lint, create unit tests, and run them as part of the execution process.

Case Study: Building the PRP Taskmaster MCP Server

Let’s make this concrete. Here’s how we built the “PRP Taskmaster” MCP server:

• Step 1: Defined the requirements in initial.md: a tool to parse PRPs and manage tasks, inspired by the existing Claude Taskmaster.
• Step 2: Generated the PRP using the MCP server template. The AI ingested both the template and the GitHub repo for Claude Taskmaster, absorbing patterns and best practices.
• Step 3: Manually reviewed the PRP for accuracy—caught and removed a risky instruction to edit secrets directly.
• Step 4: Executed the PRP. The AI scaffolded the server, implemented all 18 tools, created tests, ran them, and surfaced only two bugs, which were fixed in a second pass.
• Step 5: Used the AI to “test as a user,” walking through the full task management flow to confirm everything worked.

The result? A production-ready MCP server in two iterations—a process that would traditionally take days or weeks.

Expanding the Horizon: The Dynamis Community & Template Repositories

This is just the beginning. The Dynamis community is building out a massive repository of PRP templates for virtually every language and use case. The vision: whatever you want to build (React app, Python API, data pipeline, etc.), you’ll have a specialized, proven template as your starting point. This will democratize reliable AI coding and accelerate innovation across the board.

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Actionable Takeaways for Your Workflow

• Always validate. Be hands-on in reviewing both the PRP and code output. Trust, but verify.
• Leverage specialized templates. Start with community-vetted PRP templates to save time and improve results.
• Iterate mindfully. Expect to make minor tweaks—AI is powerful, but not perfect.
• Automate testing and validation. Use the AI to generate and run tests, and prompt it to walk through user flows.
• Contribute and collaborate. If you build or refine a template, share it back with the community.

Conclusion: The Future is Context-Driven, Template-Powered AI Coding

Context engineering, anchored by the PRP framework, is the key to unlocking reliable, scalable, and efficient AI-driven software creation. By following a structured, template-based workflow—and never skipping validation—you can ship production-grade applications in record time. Whether you’re building the next great MCP server or automating research with agent swarms, the practical application of these principles will 10x your results.

If you’re ready to take your workflow to the next level, start with a PRP template, invest the time up front in context, and always keep your hands on the validation wheel. The future of AI coding is bright—and it’s built on solid process.