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Customizing GitHub Copilot in VS Code for Professional Development: Practical Experience

August 12, 2025
8 min read

In this article, I'll share the customization methods I personally use for GitHub Copilot and provide a step-by-step guide for quickly implementing custom instructions.

GitHub Copilot has become an integral part of my workflow in VS Code, especially after the introduction of extended customization capabilities. Now any developer can adapt Copilot's behavior to the specifics of their project and team — and this truly affects code quality, routine automation, and team standardization.

Why Copilot Customization Matters

When working on multiple projects or in a distributed team, it's important that an AI assistant doesn't just generate "correct" code, but also considers your standards, architectural decisions, and style rules. Thanks to Copilot customization, I can set clear instructions that are automatically applied to every chat and code generation. This saves time on reviews, reduces bugs, and helps new team members adapt more quickly.

Why Customization is Especially Important for Generation Quality

The quality of code generated by Copilot directly depends on the context the AI receives. Of course, you can manually add your standards and rules to each request — for example, reminding Copilot about naming conventions, formatting rules, or the specifics of your project's architecture. But doing this manually for every request is inconvenient and quickly becomes tiresome.

This is where Copilot customization becomes a key tool: thanks to instruction files and settings, your rules and standards are automatically loaded into context with every Copilot interaction. You no longer need to manually repeat the same requirements — the AI will always consider them when you ask a question or set a task. This ensures that every code generation will meet your expectations and team standards.

Main Customization Features

1. Instruction Files

  • •.github/copilot-instructions.md — a unified file with project standards that is automatically applied in all Copilot chats
  • •.instructions.md — separate instructions for specific tasks
  • •Support for different levels: workspace, user, separate folders for team or personal instructions

2. Custom Instructions in Settings

In VS Code, you can configure instructions for different scenarios:

  • •Code generation: github.copilot.chat.codeGeneration.instructions
  • •Test generation: github.copilot.chat.testGeneration.instructions
  • •Code review: github.copilot.chat.reviewSelection.instructions
  • •Commit messages: github.copilot.chat.commitMessageGeneration.instructions

3. Prompt Files (Experimental Feature)

  • •Creating reusable .prompt.md files for standard tasks
  • •Support for variables and file references
  • •Files can be shared between projects and teams (e.g., template for generating React forms, security guide, onboarding instructions)

4. Best Practices

  • •Keep instructions short and specific
  • •Use multiple files for different topics and tasks
  • •Store instructions in version control — this facilitates collaboration
  • •Use the applyTo property to apply instructions only to needed file types
  • •Include links to external guides and documentation directly in prompts

Step-by-Step Guide to Customizing Copilot

Step 1: Enable Customization Support in VS Code Settings

Open settings and activate the options: chat.promptFiles and github.copilot.chat.codeGeneration.useInstructionFiles

Step 2: Create a File with Project Rules

In the repository root, create .github/copilot-instructions.md with basic standards. For example, "use async/await".

Step 3: Add Specialized Instructions

For specific tasks, create .instructions.md files in the .github/instructions/ folder. For example, a separate file for React, another for tests.

Step 4: Configure Custom Instructions Through VS Code

In settings, specify your requirements for code generation, tests, reviews, and commits.

Step 5: Optionally — Use Prompt Files

If you need to automate standard tasks — create .prompt.md files and connect them in settings.

Step 6: Control Versioning and Share Files

Store all instructions and prompts in the repository — this way the team always works according to unified rules.

GitHub Copilot Pricing

GitHub Copilot is a paid service. At the time of publication:

  • •Copilot Individual: $10 per month or $100 per year for one user
  • •Copilot Business: $19 per month per user, includes advanced security, management, and integration features

Current prices and conditions should be checked on the official GitHub Copilot website.

Alternatives to GitHub Copilot

There are alternatives to GitHub Copilot on the market that also support customization and custom usage scenarios. For example:

  • •Cursor — a modern AI-IDE with support for custom prompts and integrations
  • •Windsurf — an autonomous AI assistant for code that also allows customizing behavior for team tasks

The functionality and customization approaches of these solutions may differ from GitHub Copilot, so it's worth testing several options and choosing the one that best suits your tasks.

Personal Model Choice

Currently, my top model for code generation and text work is Claude Sonnet 4. It shows excellent results in quality, speed, and understanding task context, especially with complex queries and working with large volumes of code.

Conclusion

Customizing GitHub Copilot is not just a setup, but a tool for improving development quality and speed. From my experience, I can say: properly formatted instructions save hours on reviews and help keep the codebase clean. If you haven't tried it yet — start small, gradually adding new rules and templates as the project grows.

You can learn more about AI tools for developers in my AI tools for developers seminar repository.

For more details, I recommend the official documentation.

This article was originally published in Russian on The Tech.