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Artificial Intelligence Software Development

Accelerating Software Development with VSCode and Copilot

Group Training
Course Outline

Overview

This hands-on course teaches attendees how to use Visual Studio Code and GitHub Copilot as a practical, AI-assisted development environment. Through a combination of lecture, demonstrations, and hands-on labs, attendees will learn how to configure VS Code for productive development, use Copilot for code completion and chat-driven assistance, generate and explain code, create tests, debug defects, refactor existing applications, and review AI-assisted changes responsibly.

The course emphasizes practical developer workflows, including prompt techniques, workspace context, extensions, source control integration, terminal usage, debugging, documentation, team collaboration, and responsible AI practices. Upon completion of the course, attendees will have the skills necessary to use VS Code and Copilot to reduce development cycle time while maintaining engineering discipline and code quality.

Duration

2 Days

Who Should Take This Course

Audience

This course is designed for developers, DevOps engineers, technical practitioners, and professionals responsible for writing, reviewing, or managing software code and development workflows.

Prerequisites

Participants should be familiar with at least one programming language and basic software development concepts. Prior experience with Git is helpful but not required.

Why You Should Take This Course

This course is designed to help developers accelerate software development using Visual Studio Code and GitHub Copilot while improving code quality, collaboration, testing, and maintainability.

Course Outline

Accelerating Software Development with VSCode and Copilot

Day 1

1. Visual Studio Code Foundations

  • Overview of VS Code as a modern development environment
  • Installing and configuring VS Code for development
  • Working with extensions, settings, profiles, and keyboard shortcuts
  • Navigating projects with search, symbols, and editor tools
  • LAB: Setting up VS Code for hands-on development

2. GitHub Copilot Foundations

  • Installing and configuring GitHub Copilot in VS Code
  • Understanding Copilot’s capabilities, limitations, and responsible use
  • Exploring inline suggestions, Copilot Chat, and workspace context
  • Using Copilot to explain code, errors, and project structure
  • LAB: Exploring Copilot suggestions and chat assistance

3. Productive Coding with AI Assistance

  • Using Copilot for code completion, scaffolding, and common coding patterns
  • Writing effective comments, prompts, and chat instructions
  • Generating functions, classes, modules, scripts, and documentation
  • Working across multiple languages and project structures
  • LAB: Building application features with Copilot-assisted coding

4. VS Code Developer Workflow

  • Using the integrated terminal, tasks, and command palette
  • Managing source control with Git and GitHub from VS Code
  • Running, building, and formatting projects from the editor
  • Using snippets and editor automation to reduce repetitive work
  • LAB: Implementing a feature using VS Code workflow tools and Git integration

Day 2

5. Testing with Copilot

  • Using Copilot to generate unit tests, edge cases, and test data
  • Improving test coverage and validating expected behavior
  • Applying test-driven development techniques with AI assistance
  • Reviewing AI-generated tests for accuracy and usefulness
  • LAB: Generating and refining automated tests

6. Debugging and Defect Repair

  • Debugging applications with VS Code breakpoints, watches, and launch configurations
  • Asking Copilot to explain errors, trace defects, and suggest fixes
  • Using terminal output, logs, and stack traces as Copilot context
  • Verifying fixes with repeatable test and debug workflows
  • LAB: Debugging failures and repairing defects

7. Refactoring and Working with Existing Codebases

  • Using Copilot to understand unfamiliar code
  • Refactoring functions, classes, and modules with AI assistance
  • Applying workspace-aware prompts for multi-file changes
  • Reviewing AI-generated code for correctness, security, and style
  • LAB: Refactoring an existing codebase while preserving behavior

8. Team Practices and Advanced Copilot Workflows

  • Integrating Copilot into team development and review workflows
  • Creating documentation, README content, and developer onboarding notes
  • Managing security, intellectual property, and compliance concerns
  • Establishing practical guardrails for AI-assisted development
  • LAB: Completing an end-to-end AI-assisted development workflow
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