GH-300: GitHub Copilot

GH-300: GitHub Copilot

Offered by Linux Training

The GH-300: GitHub Copilot course at Linux Training is designed for developers, students, and IT professionals who want to enhance their coding productivity using AI-powered tools.

This course focuses on GitHub Copilot, an AI coding assistant that helps developers write code faster, reduce errors, and improve efficiency by providing intelligent code suggestions and automation.

Course Overview

This program provides a comprehensive understanding of AI-assisted development using GitHub Copilot, enabling learners to integrate AI into their coding workflow and accelerate software development.

What You Will Learn

  • Introduction to GitHub Copilot
  • AI-Assisted Code Generation
  • Writing Efficient Prompts for Coding
  • Code Completion and Suggestions
  • Debugging and Code Optimization with AI
  • Integrating Copilot into Development Environments
  • Best Practices for AI Coding

Why Choose This Course?

  • High-demand AI-powered development skill
  • Improves coding speed and productivity
  • Hands-on practical sessions
  • Real-world coding scenarios
  • Guidance from experienced trainers

Career Opportunities

After completing this course, you can explore roles such as:

  • Software Developer
  • AI-Assisted Developer
  • Full Stack Developer
  • DevOps Engineer
  • Automation Engineer

Who Can Join?

  • Developers and programmers
  • Students learning coding
  • IT professionals
  • Anyone interested in AI-powered development tools

Code Smarter with AI

Join Linux Training and learn how to use GitHub Copilot to write better code faster and boost your development productivity.

GH-300: GitHub Copilot

Modules

Use GitHub Copilot responsibly (15–20%)

Understand responsible AI principles

  • Describe risks and limitations of Generative AI tools
  • Describe ethical and responsible AI usage
  • Identify potential harms and mitigation strategies of AI usage

    • Validate and operate AI tools

  • Explain the need to validate AI output
  • Identify how to operate GitHub Copilot responsibly

    • Use GitHub Copilot features (25–30%)

    Use GitHub Copilot in the IDE

  • Enable Copilot in the IDE
  • Trigger Copilot through inline suggestions, chat, CLI, and Plan Mode
  • Exclude specific files or repositories (app knowledge)

    • Use GitHub Copilot CLI

  • Define GitHub Copilot CLI and how it benefits developers
  • Identify the steps for installing GitHub Copilot CLI
  • Describe key GitHub Copilot CLI features and commands
  • Use GitHub Copilot CLI interactively and in sessions
  • Generate scripts and manage files with GitHub Copilot CLI

    • Use GitHub Copilot features and capabilities

  • Use Agent Mode, Edit Mode, and MCP for enhanced development and workflows; manage Agent Sessions and delegate tasks to Sub-Agents for optimized context usage
  • Use Copilot for code review and coding assistance
  • Utilize Spaces, Spark, Pull Request summaries, and customizable review standards via instructions files
  • Understand the limits, options, feedback, and commands of GitHub Copilot Chat; include prompt file reuse for consistent responses

    • Manage organization-wide settings and policies

  • Configure organization-wide policy management; enable Copilot Code Review policies and manage feature availability across IDEs and github.com
  • Utilize audit log events
  • Manage subscriptions using the REST API

    • Understand GitHub Copilot data and architecture (10–15%)

    Describe data handling and flow

  • Explain data usage, flow, and sharing
  • Describe input processing and prompt building
  • Explain proxy filtering and post-processing

    • Understand lifecycle and limitations

  • Visualize code suggestion lifecycle
  • Describe limitations of LLMs and Copilot

    • Apply prompt engineering and context crafting (10–15%)

    Craft effective prompts

  • Describe prompt structure and context
  • Understand how context is determined
  • Use zero-shot and few-shot prompting
  • Apply best practices for prompt crafting

    • Engineer prompts for performance

  • Explain prompt engineering principles
  • Describe prompt process flow and chat history usage

    • Improve developer productivity with GitHub Copilot (10–15%)

    Enhance productivity and code quality

  • Use Copilot for code generation, refactoring, and documentation
  • Accelerate learning and reduce context switching
  • Generate sample data and modernize legacy code

    • Support testing and security

  • Generate unit and integration tests
  • Identify edge cases and write assertions
  • Suggest security improvements and performance optimizations

    • Configure privacy, content exclusions, and safeguards (10–15%)

    Manage privacy settings and exclusions

  • Configure content exclusions and editor settings
  • Describe ownership and limitations of outputs

    • Apply safeguards and troubleshoot

  • Enable duplication detection and security warnings
  • Resolve issues with suggestions and exclusions

    • Related Posts