Accelerate software development with GitHub Copilot while maintaining code quality, testing, and engineering standards.

AI can significantly improve developer productivity, but effective adoption requires more than code generation. This 3-day training equips software teams with practical GitHub Copilot workflows for coding, testing, debugging, documentation, code reviews, refactoring, and modernization.

  • Why get trained: Learn AI-assisted coding, testing, debugging, refactoring, code reviews, and technical debt reduction using GitHub Copilot.
  • Why it matters: Increase development speed, improve code quality, reduce repetitive work, and build consistent AI-assisted engineering practices.
  • Who should attend: Software Engineers, Technical Leads, Engineering Managers, QA Automation Engineers, DevOps Engineers, Software Architects, and Teams adopting AI-assisted development.

Gain hands-on experience through practical exercises, real-world engineering scenarios, and guided GitHub Copilot workflows. HRD Corp Claimable.

Overview

Software teams are expected to deliver high-quality applications faster than ever. While AI tools can significantly improve developer productivity, many organizations struggle to adopt them effectively without compromising software quality, maintainability, and engineering standards.

This 3-day training helps participants move beyond simple code generation and learn how to integrate GitHub Copilot into professional software engineering practices. Participants discover how AI can support implementation, testing, debugging, documentation, code reviews, technical debt reduction, and modernization initiatives.

By combining AI assistance with proven engineering practices, teams can achieve sustainable productivity improvements while maintaining quality and governance.

Skills Covered

  • Understand how GitHub Copilot is transforming modern software development
  • Apply AI-assisted development responsibly
  • Build effective developer-AI collaboration workflows
  • Accelerate coding and implementation activities
  • Generate boilerplate and repetitive code efficiently
  • Improve testing and Test-Driven Development practices
  • Use AI-assisted debugging techniques
  • Improve code quality and maintainability
  • Identify and reduce technical debt
  • Apply AI-assisted refactoring strategies
  • Improve pull request and code review workflows
  • Standardize AI-assisted development practices across teams
  • Create repeatable engineering workflows for long-term success

Prerequisites

There are no prerequisites to attend this training.

Target Audience

  • Software Engineer
  • Technical Leads
  • Engineering Managers
  • QA Automation Engineers
  • DevOps Engineers
  • Software Architects
  • Teams adopting AI-assisted development

Course Curriculum

Module 1: Modern AI-Assisted Software Development

This module introduces the evolution of AI-assisted software development and explores how GitHub Copilot is transforming modern engineering workflows. Participants learn how AI can improve productivity while preserving engineering quality and accountability. They will also understand how AI-assisted development fits into modern software engineering practices and how to establish effective collaboration patterns between developers and AI tools.

Module 2: GitHub Copilot Setup and Developer Workflows

Participants configure GitHub Copilot and explore practical workflows for implementation, navigation, code generation, documentation, and everyday development. The focus is on using Copilot effectively in real-world development environments. Participants will be able to integrate GitHub Copilot into their daily engineering workflows and improve development efficiency.

Module 3: Prompting Strategies for Software Engineers

This module focuses on communicating effectively with GitHub Copilot, crafting structured prompts, sharing context, and framing tasks. Participants learn how prompt quality directly impacts implementation quality and engineering outcomes. Participants will be able to create reliable prompts for implementation, debugging, refactoring, testing, and documentation.

Module 4: AI-Assisted Coding and Implementation

This module explores how GitHub Copilot supports feature development, code generation, architecture exploration, and implementation acceleration. Participants practice developing software collaboratively with AI while upholding engineering standards. Participants will learn to improve implementation speed without sacrificing software quality or maintainability.

Module 5: Testing, Debugging, and TDD with Copilot

Participants learn how GitHub Copilot supports unit testing, test generation, debugging workflows, and test-driven development. The module emphasizes verification, engineering discipline, and software quality. Participants will be able to improve test coverage, strengthen testing practices, and accelerate debugging workflows.

Module 6: Refactoring, Technical Debt, and Code Quality

This module focuses on maintainability and continuous improvement. Participants use GitHub Copilot to identify code smells, improve readability, reduce complexity, and support modernization initiatives. They will learn practical techniques to reduce technical debt and improve long-term maintainability.

Module 7: GitHub Workflows, Pull Requests, and Team Collaboration

This module examines how GitHub Copilot integrates into team-based development workflows, including pull requests, code reviews, collaboration standards, and engineering governance practices. Participants will understand how AI-assisted development can improve collaboration and engineering consistency across teams.

Module 8: Enterprise Capstone – AI-Assisted Engineering Transformation

Participants apply training concepts to a realistic software engineering scenario spanning implementation, testing, refactoring, review, and modernization. The capstone demonstrates how AI-assisted development can be integrated into a complete engineering workflow. Participants leave with a practical framework for adopting GitHub Copilot in enterprise engineering environments.

Dates & Locations

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Exam & Certification

Complete the AI-Assisted Software Development with GitHub Copilot Training and receive your AI-Assisted Software Development with GitHub Copilot Training Certificate of Completion after successfully completing the 3-day program. This validates your skills in applying GitHub Copilot to accelerate software development, improve testing and debugging, enhance code quality, reduce technical debt, and establish responsible AI-assisted engineering workflows.

Training & Certification Guide

Why train with Trainocate

This training teaches software professionals how to use GitHub Copilot effectively across coding, testing, debugging, refactoring, documentation, and code review activities. Participants learn practical AI-assisted development workflows that improve productivity while maintaining software quality.

No. While basic software development experience is recommended, the training covers GitHub Copilot setup, prompting techniques, and practical workflows, making it suitable for both new and existing users.

Yes. As AI-assisted development becomes increasingly common, professionals who can effectively collaborate with AI tools are likely to be better positioned for modern software engineering roles and leadership opportunities.

Participants will learn AI-assisted coding, prompt engineering for developers, testing and debugging workflows, code refactoring, technical debt management, pull request optimization, and best practices for integrating AI into development teams.

No. GitHub Copilot is designed to assist developers, not replace them. Human judgment remains essential for architecture decisions, problem-solving, security reviews, quality assurance, and business requirements analysis.

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