Course Overview
When you can only spare one day, this intensive training makes the most of it by establishing effective AI-augmented development workflows with GitHub Copilot. Unlike purely tool-focused training, this course integrates workflow design and practice evolution, covering the full spectrum of AI-augmented development—from individual productivity patterns through team integration strategies.
Through hands-on exercises with GitHub Copilot, you’ll learn how requirements engineering becomes prompt engineering, how test strategies adapt to AI capabilities, and how code review practices must evolve when AI enters the development lifecycle.
Working directly with GitHub Copilot throughout the day, you’ll practice individual workflow optimization, TDD and BDD with AI assistance, team integration patterns, and realistic approaches to measuring impact. The breadth-first approach gives you the complete picture and helps you identify which practices warrant deeper investment for your specific context.
You’ll explore agile ceremonies with AI, evolved code review approaches, common pitfalls and antipatterns to avoid, and strategies for scaling productivity across your organization. You’ll understand why most teams achieve only incremental improvements (10%) rather than transformative gains (10x), and what patterns separate the two.
Perfect for developers who need rapid practical coverage, team leads evaluating AI adoption strategies, and agile practitioners seeking to understand integration approaches. You’ll leave with concrete starter artifacts you created yourself—prompt templates, workflow checklists, team adoption plans—ready to use immediately.
Want deeper coverage? Our 2-day Deep Dive provides more hands-on practice and advanced patterns. For guided custom implementation, see our 3-day Applied workshop.
Choosing Your Tool: This course focuses on GitHub Copilot, ideal for teams already invested in the GitHub ecosystem with broad IDE support. If you work primarily in VSCode and want deeper AI integration, consider our Cursor training. For terminal-native agentic workflows, see Claude Code training. For JetBrains IDEs, see JetBrains Junie training. For tool-agnostic methodology focusing on engineering discipline and AI risk mitigation, see Disciplined AI Development.
Learning Objectives
- Transform requirements into effective AI prompts using systematic decomposition with Copilot
- Design personal and team workflows that enable significant productivity gains
- Apply test strategies (TDD, BDD) with Copilot’s AI assistance in hands-on exercises
- Conduct evolved code reviews for and with AI-generated code
- Integrate Copilot into agile development practices and ceremonies
- Create team guidelines, shared libraries, and Definition of Done for AI usage
- Recognize and avoid common pitfalls, antipatterns, and AI limitations
- Navigate data privacy and security considerations with Copilot
Topics Covered
- Foundations & Mindset Shift - Copilot capabilities, landscape comparison, why most teams achieve modest gains not transformative results
- Requirements as Prompts - Decomposition strategies, spec-driven approach, iterative refinement
- Development Workflows - TDD/BDD with AI, short iterations, agile fit, solo optimization
- Quality Assurance Evolution - Test strategies, code review changes, AI weaknesses, quality metrics
- Team Integration & Scale - Shared guidelines, agile ceremonies, collaborative workflows, measuring impact
- Pitfalls & Privacy - Antipatterns, skill atrophy risks, data collection policies, compliance
What You Get
- Personal prompt collection started during exercises
- Workflow notes capturing your preferred AI-augmented patterns
- Code review checklist for AI-generated code
- Common pitfalls reference card
- Course exercises for continued practice
These are starting points you’ll continue developing as you build your practice.