Red/Green TDD: The Secret to Reliable AI-Assisted Coding

As artificial intelligence becomes increasingly embedded in software development workflows, the challenge of ensuring AI-generated code is both functional and maintainable has taken center stage. According to Simon Willison’s influential guide on Agentic Engineering Patterns, the most effective strategy to achieve this is the disciplined application of red/green Test Driven Development (TDD). This approach, long valued by human developers, has emerged as a critical protocol for guiding AI coding assistants toward producing reliable, tested, and production-ready code.

Red/green TDD is a two-phase cycle: first, developers write automated tests that define the desired behavior of a function or module — and crucially, they confirm these tests initially fail (the "red" phase). Only then is the implementation code written to make the tests pass (the "green" phase). This method ensures that every line of code is explicitly validated against a testable requirement. For human programmers, this discipline prevents over-engineering and ensures coverage. For AI agents, it serves as a vital corrective mechanism against their inherent tendency to generate plausible but incorrect or untested code.

Large language models powering coding assistants like ChatGPT and Claude are adept at synthesizing code from natural language prompts. However, without explicit constraints, they often generate code that appears correct but fails to execute, or worse — creates unnecessary abstractions that never get used. Willison’s research demonstrates that when prompted with "Use red/green TDD," these models are far more likely to produce working, tested solutions. In controlled experiments, AI agents instructed with this shorthand consistently produced test suites that validated functionality before implementation, whereas those without the directive frequently skipped the failing-test step, leading to false positives and unverified logic.

The importance of the "red" phase cannot be overstated. Skipping it risks creating a test that passes by coincidence — perhaps because the API already had a default implementation, or because the test was written incorrectly. In such cases, the AI mistakenly believes it has solved the problem, when in reality, no actual implementation logic was exercised. By forcing the model to observe a test failure first, developers ensure the AI is not just writing code, but actively solving a problem — a fundamental distinction in quality assurance.

Moreover, the resulting test suite becomes a lasting asset. As software projects scale, regression bugs become increasingly common. A comprehensive test suite, built from the outset using red/green TDD, acts as a safety net, catching unintended side effects from future changes. This is particularly valuable in team environments where AI-generated code may be reviewed, refactored, or extended by multiple developers over time.

Practitioners are now incorporating "Use red/green TDD" as a standard prompt template in their AI-assisted workflows. For example, a prompt like "Build a Python function to extract headers from a markdown string. Use red/green TDD." yields not only working code but also a full pytest suite with edge-case coverage — something rarely produced without explicit TDD guidance. Even when using default AI environments, appending "Use your code environment" ensures the model executes tests rather than merely writing them, further enhancing reliability.

The broader implication is clear: AI is not replacing software engineering — it’s augmenting it. But augmentation requires structure. Red/green TDD provides that structure. It transforms AI from a speculative code generator into a disciplined, accountable collaborator. As organizations adopt AI coding tools at scale, those that institutionalize red/green TDD will gain a decisive edge in code quality, maintainability, and developer trust.