Mastering Project Context Files for AI Coding Agents

byJonas, Maximilian & Philip

What Are Project Context Files?

Project context files are special documentation files that provide critical information about your codebase to AI coding agents. Whether you’re using GitHub Copilot, Claude Code, Cursor, the open-source Theia IDE, or any comparable tool, these tools all support similar concepts:

• CLAUDE.md for Claude Code
• .copilot-instructions.md for GitHub Copilot
• .cursorrules for Cursor
• project-info.prompt-template for Theia AI

These files look similar to README files — they describe how to build the project, explain the architecture, document key patterns, and define code style guidelines. The key difference: they’re targeted at AI agents, not humans.

Why Do AI Coding Agents Need Project Context?

If we’re supposedly approaching artificial general intelligence, why do AI coding agents need explicit project documentation? The answer lies in how current LLMs work: they are essentially stateless.

Working with an AI coding agent without project context is like having a new developer join your team for every single task. Just like a human developer, the AI needs essential information about:

• Build processes — Which scripts to run during development vs. deployment
• Architecture patterns — How components are structured and interact
• Code style guidelines — Single vs. double quotes, indentation preferences, naming conventions
• Testing approaches — Which testing frameworks and patterns to use
• UX guidelines — When to use buttons vs. toolbars, and other design decisions

Project context files are part of the request model — the information sent to the underlying LLM with every request. This means the AI receives this critical context fresh each time, compensating for its lack of persistent memory. As shown in detail in the video, understanding the request model is essential for working effectively with AI coding agents.

Can’t AI Agents Figure This Out Themselves?

Technically, yes — but at a huge cost in efficiency and precision. As demonstrated in the video, consider two real-world examples:

1. Code style rules: If the AI doesn’t know your project uses single quotes, it might generate code with double quotes, then have to run the linter, detect the error, and fix it — wasting tokens and time with each iteration.

2. Testing frameworks: Without knowledge of your existing test structure, the AI might use the wrong testing library or style entirely, requiring additional prompts to correct.

The result? More back-and-forth, more token consumption, more time wasted, and less precise initial results.

Three Essential Techniques for Better Project Context Files

1. Don’t Trust Auto-Generated Files

Most AI coding tools can generate project context files automatically — and they’re a great starting point. But they’re far from perfect.

Why? These tools create files by:

• Analyzing your repository structure
• Reading documentation files
• Examining meta files like package.json
• Sometimes scanning source code

However, they miss critical information that only you know, for example:

• Complex build workflows — Which of dozens of build scripts are actually used during development
• Implicit conventions — Team practices not documented anywhere
• UX and design guidelines — Interface patterns that aren’t derivable from code

The evidence: As shown in the video, when we asked Claude Code to evaluate its own auto-generated file against a manually refined one, it admitted the manual version was “significantly better” — despite being much shorter (85 lines vs. 280 lines).

Recommendation: Treat auto-generation as a starting point. Manually consolidate the files by:

• Removing information that’s obvious or incorrect
• Adding missing context only you know
• Referencing other files instead of inlining everything
• Structuring content for optimal AI consumption

The manual effort pays off immediately in increased efficiency and precision.

2. Make It a Living Document

Project context files should evolve with your project. This means updating them whenever you discover something AI coding agents consistently get wrong.

This should be every developer’s responsibility during every task. When something goes wrong in a generic way — not related to your current task but to how the project works — update the project context file.

This is exactly why Dibe Coding defines this as an explicit step in the workflow. After every code review, one possible follow-up action is updating the project context. If you find something during review that should be fixed generically across the project, document it.

A common workflow makes this sustainable — without it, project context files quickly become outdated and lose their value.

3. Make It a Team Effort

Check project context files into your repository so they’re shared among all developers. This ensures everyone benefits from the accumulated knowledge and improvements.

But what if your team uses different AI coding tools? We’re currently evaluating an interesting solution: using symlinks to consolidate files.

Since most AI tools check for other project context files (e.g., GitHub Copilot looks for CLAUDE.md), you can:

1. Choose one primary project context file (preferably tool-agnostic)
2. Create symlinks from tool-specific filenames to your primary file
3. Maintain one source of truth while supporting multiple tools

This approach is still experimental, but initial results are promising. It allows teams to standardize on a single, high-quality project context file while supporting developers who prefer different AI coding tools. Watch the video to see a live demonstration of this technique.

Make AI Coding Predictable and Genuinely Productive

Understanding project context files is just one technique for working effectively with AI coding agents. Our Dibe Coding Training offers a complete, proven methodology for AI-assisted development.

Dibe Coding is a practical, repeatable workflow for getting consistent, high-quality results from any AI coding tool — Copilot, Claude Code, Cursor, Cline, Windsurf, Theia IDE, and others. Built on real projects and refined with enterprise engineering teams. The training covers not only how to create and maintain effective project context files like the ones discussed in this article, but also when and how to evolve them as your project grows.

What You’ll Learn

• Provide the right context for accurate results (including creating and maintaining project context files as living documents)
• Break down tasks into AI-friendly steps and apply a repeatable workflow
• Review and refine AI results quickly — and use those reviews to improve your project context, just like the example described earlier where review findings should trigger project context updates
• Integrate AI into existing, complex codebases
• Use any AI coding tool more effectively and confidently

“Now I’m at around a 90% success rate. AI coding went from a lottery to a predictable, high-value tool.” — Senior Developer, Early-Access Participant

The training includes step-by-step video lessons with live demos, examples from complex codebases, and lifetime access including all future updates.

👉 Get the Dibe Coding Training: Learn more about AI Coding Training

👉 For group or organizational packages, contact [email protected]

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