EARLY ACCESS — This course is actively being extended. You currently get 62 lectures covering the complete AI+TDD workflow end-to-end on a real feature. New sections are being added over the coming weeks — enrol now and get everything at the early access price.
AI can write code quickly. Trusting that code is a different problem. In real projects, it often misses edge cases, introduces subtle bugs, and produces logic that looks correct but isn’t. You end up checking everything yourself, fixing issues manually, and the speed advantage disappears. The issue isn’t speed. It’s control. Because without a clear way to define and verify behaviour, fast code generation just creates more work. This is where a structured, spec-driven workflow becomes useful. In practice, this means starting from behaviour, not implementation.
You need to define what the system should do using concrete examples, turn those into acceptance tests, then use TDD to build the code in small, verified steps. AI helps at every stage — exploring requirements, suggesting tests, generating code — but the specifications and tests define what “correct” means. You make AI work within that structure, not around it.
