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- This module explores the responsible use of AI in the context of GitHub Copilot, a generative AI tool for developers. It will equip you with the knowledge and skills to leverage Copilot effectively while mitigating potential ethical and operational risks associated with AI usage.
By the end of this module, students will be able to:
- Understand and apply the principles of Responsible AI usage.
- Identify limitations and mitigate risks associated with AI.
- Learn best practices for ensuring AI-generated code aligns with ethical standards and project-specific requirements.
- Recognize the importance of transparency and accountability in AI systems to build trust and maintain user confidence.
- GitHub Copilot is an AI-powered coding assistant that helps you write, understand, refactor, and maintain code directly from your editor and chat interface.
In this module, you will:
- Learn how GitHub Copilot can help you code by offering code suggestions.
- Learn about the various ways to trigger GitHub Copilot.
- Learn about the differences among GitHub Copilot Individual, Business, and Enterprise.
- Learn how to configure GitHub Copilot.
- Troubleshoot GitHub Copilot.
- This module highlights the basic core principles of prompt engineering and explains the best practices to create prompts quickly and effectively in GitHub Copilot. Learn the strategies that transform comments into precise code, identify the steps within GitHub Copilot's prompt processing flow, and gain an understanding of the important role that Large Language Models (LLMs) play in enhancing suggestion quality. Discover advanced techniques like role prompting and chat history management to accelerate development workflows and optimize resource usage.
By the end of this module, you're able to:
- Craft effective prompts that optimize GitHub Copilot's performance, ensuring precision and relevance in every code suggestion while accelerating development cycles.
- Understand the intricate relationship between prompts and Copilot's responses, and utilize best practices in prompt engineering including role prompting and chat history management.
- Gain insights into the underlying mechanism of how GitHub Copilot handles user prompts, from secure transmission to content filtering and context analysis, optimizing for efficient resource usage.
- Learn how to use GitHub Copilot Spaces to get context-aware, reliable answers, configure custom instructions, and optimize interactions for repositories, issues, and pull requests.
By the end of this module, learners can:
- Explain what Spaces are and when to use them versus general Copilot Chat
- Create, configure, and iterate on a Space with targeted context and custom instructions
- Apply best practices for high-quality, grounded answers within model context limits
- Use advanced features of GitHub Copilot with Visual Studio Code to make changes and updates to a Python application.
By the end of this module, you'll be able to:
- Apply slash commands to make code changes
- Interact with GitHub Copilot using the Chat feature.
- Ask questions about your project using an agent.
- GitHub Copilot Across Environments: IDE, Chat, GitHub.com, and Command Line Techniques explore the versatile integration of GitHub's AI-powered coding assistance across your entire development workflow. It covers effective use of Copilot's auto-suggestions and multiple panes in IDEs, using natural language interactions in Copilot Chat, and streamlining command-line workflows. The guide delves into optimizing prompts, understanding context-aware code generation, and customizing Copilot's behavior to accelerate code changes and automate routine development tasks across different coding environments.
At the end of this module, you are able to:
- Understand how to utilize GitHub Copilot's auto-suggestions, multiple suggestions pane, and its ability to adapt to different coding styles to accelerate code development.
- Understand how to provide context to GitHub Copilot through inline comments, block comments, doc strings, and other types of comments to enhance code generation accuracy and speed.
- Understand how to interact with GitHub Copilot through natural language conversations to generate complex code, debug issues, obtain code explanations, and streamline development workflows in real-time.
- Understand how to improve the relevance of GitHub Copilot Chat's suggestions by using scope referencing, slash commands, and agents to quickly complete routine development tasks.
- Understand how to use GitHub Copilot on GitHub.com for repository exploration, pull request assistance, issue management, and collaborative code review workflows.
- Understand how to interact with GitHub Copilot in CLI to get command explanations, suggestions, and execute commands to automate terminal workflows.
- Management and customization control are a vital part of using an AI pair programming tool. With a better understanding of the GitHub Copilot plans and their associated features, you can select the right fit for your organization's needs.
By the end of this module, you'll:
- Understand the GitHub Copilot plans and their associated management and customization features.
- Gain insight into the contractual protections in GitHub Copilot and disabling matching public code.
- Know how to manage content exclusions.
- Recognize common problems with GitHub Copilot and their solutions.
- Developer Uses Cases for AI with GitHub Copilot explores ways developers can apply AI using GitHub Copilot to enhance productivity, ultimately enabling teams to save time, improve code quality, and boost developer satisfaction.
By the end of this module, you're able to:
- Identify specific ways GitHub Copilot integrates seamlessly into developer workflows, enhancing the overall development experience and supporting individual coding preferences.
- Explore GitHub Copilot's potential impact on different stages of the Software Development Lifecycle.
- Evaluate the limitations of AI-assisted coding and measure its impact on development efficiency
- This module explores using GitHub Copilot and GitHub Copilot Chat to create unit tests. Exercises provide practical experience creating unit test projects and running unit tests in Visual Studio Code.
By the end of this module, you're able to:
Create unit tests using the GitHub Copilot and GitHub Copilot Chat extensions for Visual Studio Code.
Create unit tests that target edge cases and specific conditions using the GitHub Copilot and GitHub Copilot Chat extensions for Visual Studio Code.
Use Visual Studio Code, the .NET SDK, and the C# Dev Kit extension to create a test project and verify that your unit tests build and run successfully.
