Course Overview

When you can take the time to do it properly, this comprehensive two-day training establishes deep, effective AI-augmented development workflows with JetBrains Junie. Unlike purely tool-focused training, this course integrates workflow design and practice evolution, providing extensive coverage from individual productivity patterns through team integration strategies.

With the additional day, you’ll move beyond overview to genuine practice: more hands-on exercises with JetBrains Junie, deeper exploration of advanced patterns, nuanced techniques for handling edge cases, and time to internalize the mindset shifts required for transformative productivity gains.

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—with sufficient practice time to make these patterns second nature.

Working directly with JetBrains Junie across two full days, you’ll practice individual workflow optimization, TDD and BDD with AI assistance, team integration patterns, and realistic approaches to measuring impact. The extended format allows for deeper exploration, additional exercises to cement learning, facilitated peer discussions, and troubleshooting sessions for real challenges you’re facing.

You’ll explore agile ceremonies with AI, evolved code review approaches, common pitfalls and antipatterns to avoid, and strategies for scaling productivity. 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 seeking deep practical skills, team leads implementing AI adoption strategies, and agile practitioners integrating AI into existing workflows. You’ll leave with refined artifacts—prompt templates, workflow checklists, team adoption plans—ready to deploy. Need custom artifacts? Our 3-day Applied Workshop adds guided implementation. Only have one day? Our 1-day Fast-Track provides rapid coverage.

Choosing Your Tool: This course focuses on JetBrains Junie, ideal for developers working in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs. If you work primarily in VSCode, consider our Cursor training. For terminal-native agentic workflows, see Claude Code training. Already using GitHub Copilot? Our GitHub Copilot training deepens your existing skills. 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 Junie, practicing with real-world scenarios
  • Design and refine personal and team workflows that enable significant productivity gains through iterative hands-on exercises
  • Apply test strategies (TDD, BDD) with Junie’s AI assistance across multiple practice sessions
  • Conduct evolved code reviews for and with AI-generated code, including edge cases and quality concerns
  • Integrate Junie into agile development practices and ceremonies with team-based exercises
  • Create comprehensive team guidelines, shared libraries, and Definition of Done for AI usage
  • Recognize and avoid common pitfalls, antipatterns, and AI limitations through case study analysis
  • Navigate data privacy and security considerations with Junie in production contexts
  • Debug and troubleshoot AI-assisted development workflows when they don’t work as expected

Topics Covered

  1. Foundations & Mindset Shift - Junie capabilities, landscape comparison, why most teams achieve modest gains not transformative results, hands-on initial workflows
  2. Requirements as Prompts - Decomposition strategies, spec-driven approach, iterative refinement, advanced prompt engineering patterns, handling ambiguity
  3. Development Workflows - TDD/BDD with AI, short iterations, agile fit, solo optimization, pair programming with AI, context management
  4. Quality Assurance Evolution - Test strategies, code review changes, AI weaknesses, quality metrics, advanced testing patterns, regression handling
  5. Team Integration & Scale - Shared guidelines, agile ceremonies, collaborative workflows, measuring impact, rollout strategies, adoption challenges
  6. Pitfalls & Privacy - Antipatterns, skill atrophy risks, data collection policies, compliance, troubleshooting common issues, when NOT to use AI

What You Get

  • Personal prompt library developed and tested through multiple exercises
  • Individual workflow checklist refined through practice
  • Code review checklist for AI-generated code including edge cases
  • Test strategy reference for AI-assisted TDD/BDD
  • Common pitfalls and antipatterns guide
  • Extended exercises for continued practice

A solid foundation to build on as you deepen your AI-augmented development practice.