Test-Driven Development (TDD) has been a cornerstone of quality software engineering for decades. But as we enter the age of AI-assisted development and large language models, does TDD still hold relevance? The answer is a resounding yes—and perhaps more than ever before.
The Timeless Principles of TDD
At its core, TDD follows a simple cycle: write a failing test, write the minimum code to pass, then refactor. This red-green-refactor loop forces developers to think about requirements and edge cases before implementation. The result? Cleaner code, better design, and a comprehensive test suite that serves as living documentation.
TDD Meets AI: A Natural Partnership
When working with AI coding assistants, TDD becomes even more valuable. Here’s why:
- Clear specifications: Tests provide unambiguous requirements that AI can understand and implement against
- Verification layer: Tests act as a safety net to validate AI-generated code behaves correctly
- Reduced hallucinations: When AI knows it must pass specific tests, outputs tend to be more grounded and practical
- Iterative refinement: The red-green-refactor cycle works naturally with AI iteration
Evals: TDD for AI Systems
Perhaps the most exciting application of TDD principles is in the world of AI evaluations—commonly called “evals.” Evals are systematic tests designed to measure how well an AI model performs on specific tasks or criteria.
The parallel to TDD is striking:
| TDD Concept | Eval Equivalent |
|---|---|
| Write failing test first | Define expected behavior before training/prompting |
| Minimum code to pass | Iterate on prompts or fine-tuning until eval passes |
| Refactor | Optimize prompts while maintaining eval scores |
| Test suite | Eval suite covering multiple capabilities |
Applying TDD Mindset to Evals
Here’s how you can apply TDD thinking to your AI evaluation strategy:
1. Define Success Criteria First
Before crafting prompts or fine-tuning models, clearly define what success looks like. What inputs should produce what outputs? What edge cases matter? This is your “test specification.”
2. Create Measurable Evals
Write evals that can objectively determine pass/fail. This might include exact match comparisons, semantic similarity thresholds, or structured output validation. Avoid vague criteria that require subjective judgment.
3. Start with Failing Evals
Just like TDD, begin with evals that your current system fails. This ensures you’re building toward measurable improvement rather than just validating the status quo.
4. Iterate Incrementally
Make small changes to prompts or model configurations, run your eval suite, and observe the impact. Large changes make it difficult to understand what’s working.
5. Prevent Regression
Maintain a comprehensive eval suite that runs with every change. Just like unit tests catch regressions in code, evals catch regressions in AI behavior.
Practical Benefits
Teams adopting eval-driven development report several benefits:
- Faster iteration: Clear pass/fail criteria eliminate guesswork
- Better communication: Evals serve as a shared language between engineers, product managers, and stakeholders
- Confidence in deployment: A passing eval suite provides assurance before shipping
- Documentation: Evals document expected behavior in an executable form
Conclusion
TDD isn’t just surviving the AI age—it’s thriving. The discipline of writing tests first translates beautifully to the world of AI evals, where defining expected behavior upfront is crucial for building reliable systems.
Whether you’re writing traditional software, working with AI coding assistants, or building AI-powered products, the TDD mindset remains invaluable: specify behavior first, verify it works, then iterate with confidence.
The age of AI doesn’t diminish the importance of testing—it amplifies it.