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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.
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
Software Engineer
Technical Leads
Engineering Managers
QA Automation Engineers
DevOps Engineers
Software Architects
Teams adopting AI-assisted 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.
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.
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.
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.
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.
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.
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.
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.
The course brochure is currently being prepared and will be available soon. In the meantime, contact us to learn more about the program agenda, learning outcomes, curriculum, and training details.
Participants who complete the training will receive an AI-Assisted Software Development with GitHub Copilot Training Certificate of Completion.
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.
GitHub Copilot can help developers generate code, create tests, troubleshoot issues, write documentation, and automate repetitive tasks. This allows teams to focus more on problem-solving and delivering business value.
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.
AI-assisted development is becoming an important part of modern engineering workflows. Professionals who understand how to work effectively with AI tools can improve productivity, adapt to changing technologies, and contribute more effectively to software delivery teams.
Yes! Cancellations made within 24 hours of registration qualify for a full refund (minus payment gateway charges). Contact connect@agilemania.com for refund requests.
You can pay using debit/credit cards (MasterCard, Visa, American Express) or PayPal. Once payment is completed, you’ll receive an email confirmation.
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Learn how to use GitHub Copilot effectively across coding, testing, debugging, refactoring, documentation, and team collaboration. Build practical skills that help your team deliver software faster while maintaining engineering excellence. Register for AI-Assisted Software Development with GitHub Copilot Training Today: For corporate group training and private trainings, contact our team for more information.
