Fundamentals of GitHub Copilot

A comprehensive course that immerses participants in the rapidly evolving world of AI-paired programming, covering GitHub Copilot architecture, configuration, prompt engineering, security, and governance.

Description

This comprehensive course immerses participants in the rapidly evolving world of AI-paired programming. Designed for developers and technical leads, the course demystifies GitHub Copilot, moving beyond simple installation to explore how Large Language Models (LLMs) integrate directly into modern development workflows. Participants will gain a clear understanding of the tool's architecture and learn how to configure their Integrated Development Environments (IDEs) for maximum efficiency. Through live demonstrations, attendees will see firsthand how to navigate the "ghost text" experience, effectively manage extension settings, and integrate Copilot into their daily coding habits.

The core of the session focuses on the "Art of the Prompt." Merely turning Copilot on is not enough; developers must learn to communicate effectively with the AI to generate high-quality, secure code. We will deep-dive into prompt engineering strategies—specifically the "4 S's" of prompting—and explore advanced features such as Copilot Chat, Inline Chat, and Slash Commands. Attendees will learn how to leverage context-aware suggestions to accelerate not just code writing, but also unit testing, documentation, legacy code refactoring, and language translation.

Finally, effective AI adoption requires a strong foundation in security and governance. This course creates a critical framework for using generative AI responsibly, emphasizing the "Human in the Loop" philosophy. We will examine potential risks, including code hallucinations, hardcoded secrets, and public code matching, providing developers with the knowledge to mitigate these issues. The session concludes by addressing the most frequently asked questions regarding intellectual property, data privacy, and licensing, ensuring participants leave with a holistic view of how to use GitHub Copilot securely and productively.

Course Duration and Schedule

Instruction-Only Format

8:30 AM - 12:30 PM with a morning break.

Instruction + Hands-On Format

8:30 AM - 4:30 PM with a 1 hour lunch break and morning and afternoon breaks.

Upcoming Training

There are currently no scheduled classes for this course. If you would like to request one, click here for more information.

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Course Outline

Module 1: Class Introductions and Learning Objectives

Instructor Introduction: Background and experience with AI tools.
Course Roadmap: Overview of the 1/2 day schedule.
Learning Objectives:
- Understand the architecture and limitations of LLM-based coding assistants.
- Configure Copilot in a development environment.
- Master the "Art of the Prompt" to get accurate code generations.
- Identify security risks and how to mitigate them.
What is GitHub Copilot?
- Brief history (OpenAI Codex partnership).
- How it works (Context + LLM = Suggestion).
- Difference between Copilot Individual, Business, and Enterprise.

Module 2: GitHub Copilot Installation and Configuration

Prerequisites:
- GitHub Account requirements.
- Subscription management (Personal vs. Organization seats).
IDE Setup (Demo):
- Installing the extension in VS Code and Visual Studio.
- Signing in and authorization flow.
Extension Settings:
- Enabling/Disabling Copilot globally or for specific languages (e.g., Markdown, Python).
- Managing keyboard shortcuts.
- Critical Setting: Configuring "Public Code Match" suggestions (Allow/Block).

Module 3: Getting Started with Copilot

The "Ghost Text" Experience:
- Understanding the gray text overlay.
- Keyboard Controls:
  - Accepting suggestions (Tab).
  - Partial acceptance (Ctrl + Right Arrow).
  - Cycling through alternative suggestions (Alt + [ or ]).
The Copilot Toolbar:
- Checking status (Ready, Loading, Error).
- Opening the Copilot pane to see multiple suggestions at once (Ctrl + Enter).
First Interactions:
- Writing a function signature to trigger a completion.
- Writing a comment to trigger a completion (Comment-Driven Development).

Module 4: Prompt Engineering for Software Development

How Copilot "Sees" Your Code:
- The concept of the Context Window.
- Neighboring tabs technique (keeping relevant files open).
The 4 S's of Prompting:
- Single: Focus on one task at a time.
- Specific: detailed instructions (inputs, outputs, libraries).
- Short: Concise and clear language.
- Surround: Using open files to provide context.
Best Practices:
- Chain of Thought: Asking Copilot to "explain step-by-step."
- Iterative Prompting: Refining the request if the first output isn't perfect.
- Example-Based Prompting: Providing a sample input/output pair in the comments to guide the logic.
Demo: Refactoring a legacy code block using specific prompts vs. vague prompts.

Module 5: GitHub Copilot Features and Modes

Copilot Chat (The Side Panel):
- How to have a conversation with your codebase.
- Context variables: @workspace, @vscode, #file, #selection.
Inline Chat:
- Triggering chat directly in the editor (Cmd/Ctrl + I).
- The "Live Preview" diff view before accepting changes.
Slash Commands:
- /explain: Getting natural language explanations for complex code.
- /fix: Debugging syntax errors or logic bugs.
- /tests: Generating unit test scaffolding.
- /doc: Generating documentation and DocStrings.
Copilot Agents: Briefly touching on @terminal and specialized agents.

Module 6: Tips and Tricks to Enhance Productivity

Boilerplate reduction: Generating JSON data, SQL schemas, or HTML structures instantly.
Learning new languages: Using Copilot to translate code (e.g., "Convert this Python function to TypeScript").
Writing Regular Expressions (RegEx): Describing the pattern in plain English and letting Copilot generate the RegEx.
Unit Testing Strategy: Using Copilot to write the tests first, then the implementation.
Naming Things: Asking Copilot for variable/function naming suggestions.

Module 7: Using Copilot Securely

The "Human in the Loop" Philosophy: Why you must always review generated code.
Security Risks:
- Hallucinations: When Copilot invents libraries or functions that don't exist.
- Hardcoded Secrets: Avoiding prompts that might tempt Copilot to guess credentials.
- Vulnerable Patterns: Identifying insecure coding suggestions (e.g., SQL injection risks in generated code).
Governance & Data Privacy:
- Does GitHub use my code for training? (Distinction between Individual vs. Business/Enterprise policies).
- Telemetry settings and data retention.

Module 8: Frequently Asked Questions

Who owns the code generated by Copilot?
Can Copilot replace junior developers?
Why is Copilot suggesting code that doesn't compile?
How does Copilot handle copyright and licensing?
What is the difference between Copilot and ChatGPT?

Module 9: Conclusion

Recap of Key Takeaways.
Future Roadmap: What's coming next (Copilot Workspace, Mobile integration).
Resources for Continued Learning:
- GitHub Copilot Docs.
- Microsoft Learn modules.
Final Q&A.