GitHub Copilot: What is the AI Pair Programmer?

What is GitHub Copilot?

GitHub Copilot is an AI-powered software development assistance tool launched in 2021 by GitHub (a Microsoft subsidiary) in partnership with OpenAI. It functions as a virtual "pair programmer" that suggests code in real time as the developer types, completing lines, entire functions, and even complex code blocks based on the context of the current file and project.

Copilot was the first AI code completion tool to achieve massive adoption, with over 1.8 million paying subscribers by 2024. It demonstrated that LLMs could significantly boost developer productivity, paving the way for a whole generation of AI-assisted development tools like Cursor, Windsurf, and Claude Code.

Available as an extension for VS Code, JetBrains, Neovim, and other editors, Copilot integrates into the developer's existing workflow without forcing a tool change. For Belgian and European businesses, it represents an accessible investment (approximately 19 USD/month per developer) that can significantly reduce time spent on repetitive coding tasks.

Why GitHub Copilot Matters

GitHub Copilot has fundamentally changed the dynamics of software development, and its influence can be measured at several levels.

• Measurable productivity: according to GitHub's internal studies, Copilot allows developers to complete coding tasks 55% faster on average. Developers accept approximately 30% of proposed suggestions.
• Boilerplate reduction: repetitive tasks (getters/setters, validations, configurations, unit tests) are generated automatically, freeing time for business logic and architecture.
• Contextual learning: Copilot helps developers discover new APIs, new patterns, and libraries they were not aware of, functioning as interactive documentation.
• GitHub ecosystem integration: Copilot natively integrates with pull requests, issues, and GitHub Actions, offering automatic PR summaries, code review suggestions, and documentation assistance.
• Industry standard: Copilot has defined the standard for AI development assistance. Understanding how it works and its limitations has become an essential skill for any professional developer.

How It Works

GitHub Copilot relies on LLMs specifically trained or adapted for code generation. Originally based on OpenAI Codex (a GPT-3 derivative trained on public code repositories), Copilot now uses more advanced GPT versions and even allows the use of Claude (Anthropic) in certain configurations. The model analyzes the context of the current file, open files, and comments to generate relevant suggestions.

The process works in several steps. When the developer types code or writes a comment, the Copilot extension sends the context (surrounding code, file names, imports) to the server. The LLM generates multiple suggestions ranked by relevance, and the extension displays the best one as grayed-out ghost text. The developer can accept the suggestion with Tab, reject it, or navigate between alternatives.

Copilot Chat, introduced later, adds a conversational interface allowing developers to ask questions about code, request explanations, generate tests, or refactor code via natural language dialogue. This evolution brings Copilot closer to Cursor's capabilities, although with less deep contextual integration.

Concrete Example

At KERN-IT, GitHub Copilot is part of the standard developer toolkit. The team uses it daily to accelerate repetitive tasks: generating unit tests from existing functions, writing Django serializers, creating standardized React components, and documenting code. A senior developer estimated a 20-30% time saving on routine coding tasks, freeing time for architecture and code review.

A notable use case: during the development of a RESTAPI for a logistics client, Copilot generated CRUD endpoints, validations, and integration tests from data models, reducing initial development time by several days. The team then focused its efforts on complex business logic and performance optimization, the areas where human expertise delivers the greatest value.

Implementation

1. Activate Copilot: subscribe via GitHub (Individual, Business, or Enterprise). The Business offering provides management features for organizations, including policy control and file exclusion.
2. Install the extension: add the GitHub Copilot and Copilot Chat extension in your editor of choice (VS Code, JetBrains, etc.). Authentication is done via your GitHub account.
3. Configure policies: for businesses, define usage rules: exclude sensitive files, disable data collection for training, configure suggestion filters.
4. Learn to collaborate with AI: write descriptive comments to guide suggestions, break code into small well-named functions, and use Copilot Chat for complex tasks.
5. Validate systematically: never blindly accept suggestions. Check logic, security (injection, secrets management), and compliance with project standards.
6. Measure impact: track productivity metrics (delivery speed, bug rates) to quantify the tool's return on investment.

Associated Technologies and Tools

• Direct competitors: Cursor (full IDE), Windsurf (formerly Codeium), Amazon CodeWhisperer, Tabnine, JetBrains AI Assistant
• Underlying LLMs: GPT-4 (OpenAI), Claude (Anthropic) — Copilot now allows model selection in certain configurations
• GitHub ecosystem: GitHub Actions, GitHub Issues, Pull Requests — Copilot enriches every component of the platform
• Complementary extensions: ESLint, Prettier, SonarLint for validating AI-generated code
• CLI tools: GitHub Copilot CLI for AI-assisted shell command generation

Conclusion

GitHub Copilot has democratized AI assistance in software development, becoming an indispensable tool for professional development teams. At KERN-IT, it is used as a daily accelerator that complements developer expertise without replacing it. The key to effective use lies in the ability to guide the AI with precise context, validate each suggestion, and focus human effort on high-value aspects: architecture, security, and business logic.