Use Test-Driven Development with AI-Generated Code
Relying on AI to generate code and tests at the same time is risky. The AI can misinterpret requirements and produce code with subtle bugs. Manually reviewing large blocks of AI-generated code is slow and error-prone. Test-Driven Development (TDD) provides the perfect framework to solve this.
You should adopt a Test-Driven Development (TDD) workflow for AI-assisted development. The human developer must write the tests first to define the requirements, and then use the AI to generate the code that passes those tests.
TDD is more critical in the age of AI, not less. It provides the essential framework to evaluate and refine AI-generated code, solving the pain-point-01-almost-correct-code problem. The test cases act as a protocol or specification, ensuring that every piece of AI-generated code aligns with the project's requirements before it's even written. This workflow transforms the developer's role from a passive code-paster into an "active, understanding architect". The developer focuses on the high-level business logic (by writing tests), and the AI does the "grunt work" of implementation. This human-led process ensures design integrity and catches the subtle security and logic errors that AI often creates when left unguided. It's the most effective way to "tame the AI" and ensure its output is deterministic and correct.
This workflow is the ideal best practice for generating any new, testable unit of code, such as a new function, class, service, or API endpoint. It is the most robust method for ensuring functional correctness from the very beginning.
Adopt a modified "Red-Green-Refactor" TDD cycle: Red: The developer writes a new unit test that defines the desired functionality. The test fails (as it should) because the code doesn't exist yet. Green: The developer prompts the AI, providing the test code and the error message as context. (e.g., "Write the code that makes this test pass: [paste test code and failure output]"). Iterate: The AI generates the code. The developer saves it and runs the unit tests. If the tests fail, the developer feeds the new failure output back to the AI (e.g., "That was close, but it failed with this error: [paste error]. Please fix it."). Refactor: Once the test passes ("Green"), the developer (or the AI) can refactor the code for readability and maintainability, knowing the tests will protect against regressions. This loop makes the developer faster by automating the undifferentiated heavy lifting, while keeping them in full control of the business logic and quality.
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.