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GitHub Copilot Fundamentals
Course Description
GH-300: GitHub Copilot Fundamentals
Course Curriculum
SEO Title: GitHub Copilot Course (GH-300): Learn AI Pair Programming, Prompt Engineering & Responsible AI
Meta Description: Master GitHub Copilot with this hands-on GH-300 course. Learn responsible AI, code completion, Copilot Chat, prompt engineering, and advanced techniques to code faster in any language or IDE.
Primary Keywords: GitHub Copilot course, GH-300 certification, GitHub Copilot training, AI pair programming, prompt engineering for developers
Secondary Keywords: Copilot Chat, AI code completion, responsible AI, GitHub Copilot in VS Code, GitHub Copilot setup, Microsoft Learn
Course Overview
Learn how to code faster and smarter with GitHub Copilot, the AI-powered coding assistant. This blended course combines instructor-led training with Microsoft Learn resources. You’ll cover responsible AI, Copilot features, prompt engineering, and advanced workflows, and you’ll be ready for the GH-300 certification exam.
| Level | Beginner to Advanced |
| Format | Instructor-led + Microsoft Learn self-paced content |
| Prerequisites | Basic programming knowledge in any language; a GitHub account |
| Audience | Developers, DevOps engineers, students, tech leads, and IT professionals |
| Certification | GH-300: GitHub Copilot |
Course Curriculum
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What is responsible AI? Ethics, transparency, and accountability
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Risks of AI-generated code: bias, security, licensing, and accuracy
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Reviewing and validating Copilot suggestions
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Aligning AI output with project requirements and compliance needs
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What GitHub Copilot is and how it works
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Plans and licensing: Individual, Business, and Enterprise
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Installing and configuring Copilot in VS Code, Visual Studio, JetBrains, and more
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Common setup issues and troubleshooting
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Autocomplete-style code suggestions
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Using Copilot Chat: ask, explain, fix, and refactor
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Inline chat, slash commands, and context references
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Generating tests, documentation, and comments
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Anatomy of an effective prompt
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Providing context, examples, and constraints
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Iterating and refining prompts
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Common prompt mistakes and how to avoid them
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Role of large language models in code generation
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How context is gathered and processed
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Data handling, privacy, and content exclusion
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Why Copilot gives different results in different situations
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Using Copilot with Python, JavaScript, C#, Java, SQL, and more
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Working in IDEs, the CLI, and GitHub.com
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Adapting to different frameworks and workflo
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Refactoring and modernizing legacy code
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Debugging, code review, and security checks with Copilot
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Automating repetitive tasks and testing workflows
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Team best practices and measuring productivity gains
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Recap of key concepts by exam domain
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Practice questions and exam strategies
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Next steps and Microsoft Learn resources
Course Eligibility
Audience profile
Audience profile for this course is the following:
• Developers looking to enhance their coding efficiency with AI-powered tools.
• Programmers interested in learning about responsible AI usage and ethical standards.
• Software engineers seeking to integrate GitHub Copilot into their development workflows.
• Coders wanting to improve their prompt engineering skills for better AI-generated code
suggestions.
Audience prerequisites
The audience for this 1-day course consists of developers, programmers, software engineers, and coders
who want to enhance their coding efficiency, learn about responsible AI usage, and integrate GitHub
Copilot into their development workflows.
NOTE: The exercise activities in this 1-day class are Advanced and require intermediate knowledge of Git
and GitHub functions and features.
Candidates should have the following:
• Basic understanding of programming concepts and experience with at least one programming
language.
• Familiarity with integrated development environments (IDEs) and version control systems like
GitHub.
• Foundational knowledge of AI and machine learning principles
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