programming-workflow

To enforce a TDD workflow with AI, you need a structured process of preparation, design, failing tests, minimal implementation, and review. This approach uses red-green-refactor iteration to ensure maintainable code and predictable behavior.

The best way to prevent brittle AI code is to apply stage-gated outputs, requiring explicit conceptual test case design and MECE-oriented task decomposition before writing any executable production code.

Start TDD red-green-refactor iteration by decomposing requirements into independent sub-tasks, designing classes and test cases conceptually, writing failing tests, and then implementing minimal code to satisfy them.

Yes, you can use TDD stages for bug fixes and code changes where maintainability is required. The workflow requires designing failing tests that capture the bug before writing minimal implementation code.

You should not use design-first test case design when a task bypasses the need for maintainability and predictable behavior, as this workflow strictly prohibits writing any implementation during the design stage.