Course Overview

AI coding assistants generate code faster than humans can review. Without a systematic approach, developers find themselves perpetually chasing—producing volume without confidence in correctness. Test-later becomes catch-up-never.

This training repositions Test-Driven Development as the essential control mechanism for AI collaboration. You’ll learn to use tests not just for verification, but as executable specifications that constrain AI behavior, build trust through systematic code path exercise, and persist as durable guidance across sessions. The result: high-volume AI generation paired with human-level confidence.

Through hands-on exercises with real coding challenges, you’ll master the red-green-refactor rhythm with AI as your implementer, learn the AAA pattern (Arrange-Act-Assert) as a structure for clear specifications, and practice three-stage test development that maintains focus despite AI generation volume. You’ll understand why TDD becomes more important—not less—when working with AI, and how to stay at the conceptual level while AI handles implementation details.

The course addresses common AI pitfalls: happy path bias, success theater, logic inversions, and the assertion-weakening trap. You’ll learn to recognize these patterns and write specifications that catch them. Perfect for developers with AI coding tool experience who want to combine AI productivity with engineering confidence.

Choosing Your Path: This course focuses on TDD methodology that works with any AI coding assistant. If you want training centered on a specific tool, see our dedicated courses: Cursor for VSCode users, Claude Code for terminal-native workflows, GitHub Copilot for GitHub ecosystem teams, or JetBrains Junie for JetBrains IDE users. For a broader methodology course covering XP practices, risk mitigation, and team resilience, see XP/TDD + AI.

Learning Objectives

  • Articulate why TDD becomes more important, not less, when working with AI
  • Apply the AAA pattern (Arrange-Act-Assert) consistently in test design
  • Use tests as both trust-building mechanisms and permanent specifications for AI
  • Work at a conceptual level while AI handles implementation details
  • Apply three-stage test development (ideas → outlines → implementations)
  • Maintain focus and control despite high-volume AI generation
  • Recognize common AI bug patterns and design tests that catch them

Topics Covered

  1. The Catch-Up Problem - Why test-later fails with AI; the illusion of productivity
  2. Tests as Specifications - Dual purpose: executable constraints and trust through exhaustion
  3. The AAA Pattern - Arrange-Act-Assert as structure for clear, effective specifications
  4. Red-Green-Refactor with AI - The collaboration rhythm; you specify, AI implements
  5. Three-Stage Development - Ideas → outlines → implementations; staying organized at scale
  6. Common AI Bug Patterns - Happy path bias, success theater, logic inversions, assertion weakening
  7. Context Management - Tests as anchors across sessions; specifications that persist

What You Get

  • Personal TDD workflow checklist for AI collaboration
  • Test design principles reference
  • Three-stage development template
  • Sample kata exercises with AI-ready test suites
  • Common AI bug patterns reference card