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 of AI-augmented development—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 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 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 of each topic area, additional exercises to cement learning, facilitated peer discussions to surface insights, and troubleshooting sessions to address real challenges you’re facing. 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—with enough depth to apply these insights to your specific context. Perfect for developers seeking deep practical skills with Junie, team leads implementing AI adoption strategies, and agile practitioners integrating AI into existing workflows, you’ll leave with more developed artifacts you created yourself—prompt templates, workflow checklists, team adoption plans—ready to deploy and refined through extensive practice. Need customized artifacts for your team’s specific context? Our 3-day JetBrains Junie Applied Workshop adds a third day of intensive guided workshop where you build production-ready prompt libraries and workflows tailored to your organization, supported by an experienced practitioner. Only have one day available? Our 1-day JetBrains Junie Fast-Track provides rapid practical coverage of the complete landscape with starter artifacts.

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

Participants create refined, practice-tested artifacts through extended hands-on exercises:

  • Personal prompt template library for common development tasks, validated through multiple exercises
  • Individual workflow checklist for AI-augmented development with refinements from practice
  • Code review checklist adapted for AI-generated code including edge cases
  • Team adoption plan with detailed rollout strategy, implementation guidelines, and risk mitigation
  • Definition of Done for AI usage adapted to your team’s context and validated against scenarios
  • Team guidelines document for .junie/guidelines.md with comprehensive code standards

These artifacts are more developed and tested than the 1-day versions, ready for immediate deployment with confidence built through extensive practice.