Test-Driven Development

Test-driven development — TDD — is a software engineering practice where you write a failing test first, then write the minimum code needed to make that test pass, then refactor. This cycle — red (failing test), green (passing test), refactor — repeats for each piece of functionality. TDD has found new relevance in the AI era as a way to guide and validate AI-generated code.

The TDD cycle

• Red: Write a test that describes the behaviour you want. Run it. It fails because the code does not exist yet.
• Green: Write the simplest code that makes the test pass. Do not optimise or add features — just make it work.
• Refactor: Clean up the code while keeping all tests passing. Improve structure, remove duplication, and clarify naming.
• Repeat: Move on to the next behaviour and start the cycle again.

Why TDD matters for AI-assisted development

When AI writes code, TDD provides critical safeguards:

• Specification as tests: Tests serve as an unambiguous specification. Instead of describing what you want in natural language (which the AI might misinterpret), you describe it in executable tests that either pass or fail.
• Automatic validation: After the AI generates code, you immediately know whether it works by running the tests. No manual checking required.
• Regression protection: As the AI modifies code in later sessions, existing tests catch any breakage. This is especially important because AI does not remember what it built previously.
• Incremental correctness: Building functionality one test at a time keeps the AI focused on small, verifiable steps rather than generating large blocks of untested code.

TDD with AI in practice

A typical AI-assisted TDD workflow looks like this:

• You write the test (or describe the test to the AI and review it).
• You ask the AI to write code that makes the test pass.
• You run the tests to verify.
• You ask the AI to refactor if needed.
• You write the next test and repeat.

When TDD is most valuable

• Business logic: Rules, calculations, and decision trees that must be precisely correct.
• API integrations: Ensuring data is transformed and passed correctly between systems.
• Data processing: Pipelines where incorrect transformations could corrupt data.
• Any code that AI generates: TDD is the most reliable way to ensure AI-generated code does what you intended.

Common objections

"Writing tests first takes longer." In traditional development, this is debatable. In AI-assisted development, it is clearly false — the AI writes the implementation code in seconds, so the only human investment is in writing clear tests, which are also the specification.