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 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 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 the full range of AI-augmented development practices: 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 and evolve as you deepen your practice. Want to take the time to do it properly? Our 2-day GitHub Copilot Deep Dive provides deeper coverage with more hands-on practice, advanced patterns, and nuanced techniques. For guided custom implementation, see our 3-day Applied workshop.

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

  1. Foundations & Mindset Shift - Copilot capabilities, landscape comparison, why most teams achieve modest gains not transformative results
  2. Requirements as Prompts - Decomposition strategies, spec-driven approach, iterative refinement
  3. Development Workflows - TDD/BDD with AI, short iterations, agile fit, solo optimization
  4. Quality Assurance Evolution - Test strategies, code review changes, AI weaknesses, quality metrics
  5. Team Integration & Scale - Shared guidelines, agile ceremonies, collaborative workflows, measuring impact
  6. Pitfalls & Privacy - Antipatterns, skill atrophy risks, data collection policies, compliance

What You Get

Participants create practical starter artifacts during hands-on exercises:

  • Personal prompt template library for common development tasks
  • Individual workflow checklist for AI-augmented development
  • Code review checklist adapted for AI-generated code
  • Team adoption plan outline with rollout strategy and implementation guidelines
  • Definition of Done starter for AI usage adapted to your team’s context
  • Team guidelines template with code standards and Copilot usage patterns

These artifacts serve as foundation points you’ll evolve and expand as you deepen your AI-augmented development practice.