GitHub CopilotStudy guide

Understand the risks and limitations of generative AI tools, ethical and responsible use, and the potential harms of AI usage with mitigation strategies—the foundation for using GitHub Copilot correctly and safely.

Understand why and how to validate AI output, and how to operate GitHub Copilot responsibly across an org/team (policy, education, integration into workflows).

Understand enabling Copilot in the IDE and using its surfaces—inline suggestions, Copilot Chat, Plan Mode, and the CLI—plus installing GitHub Copilot CLI with its key commands, and excluding specific files/repositories (content exclusion).

Understand advanced development via Agent Mode, Edit Mode, and MCP (Model Context Protocol); managing agent sessions and delegating to sub-agents; code-review assistance; and consistent help via Spaces, Spark, Pull Request summaries, and instructions/prompt files.

Understand organization-wide policy management, enabling Copilot Code Review policies, managing feature availability across IDEs and github.com, using audit log events, and managing subscriptions via the REST API.

Understand how Copilot uses, flows, and shares data; how input is processed and the prompt is built; and the role of proxy filtering and post-processing.

Visualize the lifecycle of a code suggestion from generation to accept/reject, and understand the limitations of LLMs and Copilot (context dependence, knowledge cutoff, probabilistic generation, no correctness guarantee).

Understand prompt structure and how to provide context, how Copilot determines context, zero-shot/few-shot prompting, and best practices for crafting effective prompts.

Understand prompt-engineering principles (clarity, specificity, iteration, decomposition), the prompt process flow, and how chat history is used in Copilot Chat—its benefits and caveats.

Understand using Copilot for code generation, refactoring, and documentation, plus accelerating learning, reducing context switching, generating sample data, and modernizing legacy code.

Understand using Copilot to generate unit/integration tests, identify edge cases and write assertions, and suggest security improvements and performance optimizations—along with the need to validate.

Understand configuring content exclusions and editor settings, and the ownership and limitations of Copilot’s output (whose it is, matches with public code, licensing caveats).

Understand enabling safeguards such as duplication detection (for suggestions matching public code) and security warnings (for vulnerable suggestions), and troubleshooting issues with suggestions and exclusions.