We explore how test-driven development (TDD) remains essential—perhaps more than ever—when working with AI coding tools. Luca shares his evolved workflow using Claude Code, breaking down how he structures tests in three phases: test ideas, test outlines, and test implementations. We discuss why TDD provides the necessary control and confidence when AI generates code, how it prevents technical debt accumulation, and why tests serve as precise specifications for AI rather than afterthoughts.
The conversation covers practical challenges like AI's tendency toward "success theater" (overly generous assertions), the importance of maintaining tight control over code quality, and why the bottleneck in AI-assisted development isn't code generation—it's expressing clear intent. We also touch on code spikes, large-scale refactorings, and why treating AI development as pair programming keeps you in the driver's seat. If you're wondering whether TDD still matters when AI writes your code, this episode makes a compelling case that it matters more than ever.
"As far as I am concerned, test-driven development is just about writing prompts for the AI that it can then use to build what you want it to build." — Luca
"If you expect that a five-line prompt resulting in 10,000 lines of code will not result in 9,995 lines of uncertainty, you're just deluding yourself." — Luca
"You can be five times faster than you were before and still maintain a very high production level quality code, but you probably can't be a hundred times faster." — Jeff
You can find Jeff at https://jeffgable.com.
You can find Luca at https://luca.engineer.
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Ryan Torvik and Luca have started the Embedded AI podcast, check it out at https://embeddedaipodcast.com/
