This course is designed to be interactive, with hands-on activities making up 50% of the time. It provides both beginner and intermediate developers with the skills to leverage GitHub Copilot effectively.
Course Objectives:
This course is designed for developers, DevOps professionals, software engineers, and IT professionals who use or plan to use GitHub as a core part of their software development and version control processes. Participants range from beginners seeking foundational GitHub skills to experienced professionals looking to enhance their collaboration, automation, and CI/CD workflows.
Lesson 1: Introduction to GitHub Copilot
· Overview of AI & LLMs
o What is AI & LLMs and how does it help developers?
· Overview of GitHub Copilot
o What is GitHub Copilot?
o Benefits of AI-powered coding assistance.
o Supported programming languages and tools.
· Setting Up Copilot
o Prerequisites: GitHub account, supported IDEs (VS Code, JetBrains).
o Installing and configuring GitHub Copilot.
o Troubleshooting common installation issues.
· Lab:
o Install and configure GitHub Copilot in a development environment.
o Generate your first suggestions using simple code snippets.
Lesson 2: Using GitHub Copilot for Coding Writing Code
o Using code completion
o Autocomplete features.
o Writing functions and classes.
o Generating repetitive patterns and boilerplate code.
· Enhancing Code with Copilot
o Refactoring suggestions.
o Exploring language-specific features (Python, JavaScript, etc.).
· Lab:
o Write and refactor a simple program using Copilot.
o Experiment with multi-line code suggestions.
Lesson 3: Advanced Features and Customization
· Prompt engineering in GitHub Copilot
o Controlling suggestion frequency and relevance.
o Providing effective prompts for better suggestions.
□ Breaking down complex tasks
· Debugging and Testing with Copilot
o Using Copilot for unit test generation.
o Debugging tips with AI assistance.
· Lab:
o Generate and execute unit tests.
o Debug an application with Copilot’s suggestions.
Lesson 4: Real-World Use Cases and Best Practices
· Use Cases
o Code and Security Reviews
o Generating documentation.
o Building APIs and data pipelines.
o Accelerating front-end and back-end development.
· Best Practices
o Combining Copilot with traditional tools.
o Avoiding over-reliance on AI.
□ Remembering you are ultimately responsible for the code
□ Static Analysis & Pay attention to compiler warnings
o Ethical considerations and intellectual property.
· Lab:
o Work on a real-world mini-project using GitHub Copilot.