TDD as AI Control
A one-day course on making AI useful instead of dangerous
Course Details
- Duration: 1 day
- Format: Hands-on
- Focus: Practical examples
- Level: Intermediate
- Outcome: Disciplined AI use
Prerequisites
- Experience with AI coding tools (Claude, Copilot, etc.)
- Understanding of automated testing concepts
- Proficiency in at least one programming language
The Problem You Don't Know You Have
AI produces code that looks right, sounds certain, and is wrong more often than you think.
You've probably used Claude or Copilot to write code. Maybe it went well. Maybe it produced something that passed a few manual checks and shipped. Then, three weeks later, you discovered it was subtly wrong—an edge case missed, a null reference waiting to happen, an assumption baked into the implementation that nobody noticed.
This is the AI problem nobody talks about: confidence without correctness.
The solution is not better prompts. It's not more context. It's not a smarter model. The solution is Test-Driven Development—not as a safety net, but as a control mechanism.
What You'll Learn
This course will show you how TDD transforms AI from a liability into a tool you can actually trust.
How to use TDD to guide AI toward solutions you actually want
Write tests that prevent AI from filling gaps with dangerous assumptions
Explore and refactor aggressively with tests as your safety net
Spot when AI over-engineers, under-implements, or invents complexity
Course Structure
- What TDD actually is and why AI needs it
- The Red-Green-Refactor cycle in practice
- How tests catch AI assumptions before they become bugs
- Live examples: watching AI get it wrong and making it right
- Using TDD to explore design options safely
- Adding features without breaking existing behaviour
- Refactoring with confidence
- Hands-on: building a feature test-first with AI
- The mistake everyone makes: asking AI for too much
- Writing tests that define one small behaviour at a time
- Building complex systems from simple constraints
- Exercise: service layer development with tight test control
- Common patterns: over-mocking, premature abstraction, complexity invention
- How to recognize when AI is following inappropriate patterns
- Tightening constraints to get the design you want
- Testing behaviour vs testing implementation
- Test-first API design
- Test-first domain modelling
- Test-first refactoring of legacy code
- Practical workshop: apply patterns to your own code
Key Takeaways
"Tests are not a safety net. They are the specification. The AI is just the typist."
Control the Contract
Learn to use tests as constraints that AI must satisfy, ensuring correctness by design
Move Fast Safely
Refactor aggressively and experiment freely with tests proving behaviour hasn't changed
Catch Assumptions
Stop AI from filling gaps with dangerous assumptions by making every behaviour explicit
Build Trust
Create a workflow where AI becomes a reliable implementation tool, not a source of subtle bugs
Who Should Attend
This course is for developers and technical leads who:
- Are already using AI coding tools but struggle with confidence in the output
- Have experienced AI-generated bugs that slipped through code review
- Want to accelerate development with AI without sacrificing quality
- Understand testing concepts but need to apply them effectively with AI
- Lead teams adopting AI tools and need a disciplined approach
What This Course Is Not
- A worship of TDD or AI - we're pragmatic about both
- A tutorial on testing frameworks or tools - you'll use what you know
- A comprehensive TDD course - it's focused specifically on AI control
- Beginner-friendly - you need existing coding and testing experience
Ready to Make AI Useful Instead of Dangerous?
Book this course for your team or enquire about availability for open sessions.
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