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- Your copilot is dumb because you never taught it your codebase
Your copilot is dumb because you never taught it your codebase
Stop fighting your AI, start teaching it.
Sanket Gawas
July 04, 2025 • Estimated Reading Time: 7 minutes
Why does your copilot code like a clueless intern?
Most developers that I know struggle with GitHub Copilot generating plausible looking code that requires extensive reprompting to match their project's architecture and standards. The core issue isn't capability but context. Without understanding your specific codebase and team conventions, Copilot operates like a talented new hire who hasn't learned your project's unique requirements yet.
The solution: Project specific Instructions
GitHub provides a straightforward solution that many developers haven't discovered: the .github/copilot-instructions.md file. This single configuration file transforms Copilot's behavior by providing project specific context that dramatically improves suggestion quality.
When you create this file in your repository root, Copilot reads and applies these instructions to every suggestion it generates. The difference in output quality becomes immediately apparent.
Practical Implementation examples
The community has created extensive resources for implementing effective Copilot instructions:
Domain Driven Design Focus: Instructions emphasizing .NET development patterns, comprehensive testing strategies, and test driven development workflows help maintain architectural consistency across large codebases. More here
Framework Specific Guidance: Detailed examples for TypeScript and React projects demonstrate how to configure both settings based and file based instruction methods, ensuring suggestions align with modern frontend development practices.
Multi Language Support: Community maintained templates cover C, C#, C++, Go, Java, JavaScript, Python, Rust, Swift, and TypeScript, along with framework specific instructions for tools like Cobra CLI, Node.js, and infrastructure-as-code platforms like Terraform. Find here.
Real World Results: Developers report significant improvements in code quality, with suggestions that follow modern coding standards, implement security best practices, and require minimal revision before integration. Reddit thread here
The most effective copilot instructions files typically include these high level sections.
## Project Overview Section
- Technology stack and frameworks
- Architectural patterns and principles
- Key dependencies and tools
## Coding Standards Section
- Naming conventions and formatting rules
- Language specific preferences
- Code organization patterns
## Testing and Quality Section
- Testing frameworks and methodologies
- Coverage requirements and assertion preferences
- Error handling patterns
## Team Workflow Section
- Documentation standards
- Commit message formats
- Review criteria and focus areasI've shared a template that you can use as a foundation for your own Copilot instructions below.
📌 AI Term of the Day: MCP (Model Context Protocol)
What it is:
A universal, open source standard (from Anthropic, Nov 2024) that lets LLMs securely connect to tools, data, and APIs like a USB‑C style interface for AI systems.
Where it’s used:
Dev tools / IDEs – agents can read repos, run CI tests, pull docs
Apps & databases – access live CRM, customer data, internal APIs
Content workflows – fetch web data or search across knowledge bases
Real world example:
Instead of copying and pasting data into ChatGPT, an MCP enabled AI assistant can directly connect to your company's customer database to answer questions like "What's our top customer's recent order history?" or access your GitHub repository to debug code issues in real time
😇 Inspiring
Anthropic team dives into the Model Context Protocol (MCP), the standard that's changing how AI applications connect with external data and tools.
MCP by Anthropic
As models gain more intelligence and can handle longer running tasks, the inherent primitives built into MCP, such as those related to statefulness and sampling, are poised to become more widely utilized in an agent's world.
A significant future enhancement for MCP is the registry API, designed to allow models to proactively search for and integrate additional servers on demand, thereby fostering a more "agentic loop" where the model can expand its own operational context.
Further supporting sophisticated, autonomous AI applications, MCP's future developments also include making it easier to manage long running tasks and implementing elicitation, which enables servers to request more information from the user when needed
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