From Zero to Hero: Working with GitHub Copilot

From Zero to Hero: Working with GitHub Copilot

ABOUT THE COURSE:

Getting genuinely useful results out of GitHub Copilot requires more than just accepting inline autocomplete suggestions. This course explores how to effectively integrate Copilot into your daily engineering workflow, starting from the fundamentals of generative AI and large language models. You will begin by configuring Copilot across Visual Studio and other IDEs before moving into prompt and context engineering. You’ll learn how Copilot handles automatic context and RAG in chat, and how to shape its output using persistent context engineering through custom instructions, user-level memories, and reusable prompt files. From there, we explore the different chat modes to understand and build features. You’ll use Ask mode to navigate an unfamiliar codebase, then switch to Plan mode and Agent mode to implement a repository pattern and a unit of work. We also cover how to handle scenarios where Agent mode gets it wrong, using it instead to refactor code, generate tests, and document your repository.

Once you understand the core workflow, the course moves into evaluating and selecting the right AI models based on LLM benchmarks and cost management. You will learn how to leverage specialized built-in agents to fix errors from the Error List, debug exceptions with @debugger, optimize performance with @profiler, and update code with @modernize. Next, we cover the full pull request lifecycle, using Copilot directly on GitHub to generate commit messages, summarize PRs, and conduct code reviews. Finally, you will master extending Copilot to fit your specific architecture. We cover the extensibility spectrum by building custom agents and skills, and then go deep into MCP servers. You’ll learn exactly what MCP is, discover existing servers, and build your own MCP server from scratch—scaffolding tools, wiring it up, and combining it with custom agents to integrate Copilot directly with your external systems.

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