I used to treat Test-Driven Development (TDD) as busywork. Write a failing test. Watch it fail. Write code to pass it. Rinse and repeat. It always felt like it slowed me down.
Then AI changed everything. Tools like GitHub Copilot can now generate implementation and comprehensive tests in seconds. Suddenly, the mechanical ritual of TDD seemed… optional. But the more I experimented, the more I realized something important: TDD’s real value was never the mechanics—it was the thinking behind it.
Why TDD Exists
Consider a loan approval system. A developer might rush a method to production, thinking, “I’ll handle testing later.” Inevitably, bugs surface. Investigations trace back to that very method. The cost? Time, reputation, and headaches.
TDD was designed to prevent exactly that. Its purpose isn’t writing tests—it’s forcing clarity:
- Positive and negative scenarios
- Boundary conditions
- Exceptional cases
Tests act as living specifications. Production code exists to satisfy them. The outcome: better-designed methods, clearer APIs, fewer surprises.
AI Can Do the Manual Work
I ran an experiment: I asked Copilot to role-play a developer following strict TDD. It wrote tests first, implemented code to pass them, and did it in seconds. Manual TDD would have taken me hours.
If AI writes tests for me, how do I ensure I still get the design-thinking benefits?
The Specification Is What Matters
Classical TDD insists tests come first. The test must fail. Only then do you write code.
When AI generates both implementation and tests simultaneously, the order is no longer the key. What matters is clear, human-readable specifications.
Instead of writing tests first, I now create detailed method stubs with comments:
/// <summary>
/// Evaluates loan approval based on credit score and debt-to-income ratio.
/// </summary>
/// <param name="creditScore">Must be between 300 and 850.</param>
/// <param name="debtToIncomeRatio">Must be between 0 and 1.</param>
/// <returns>True if approved (credit >= 650 AND DTI <= 0.43).</returns>
/// <exception cref="ArgumentException">Thrown if inputs are outside valid ranges.</exception>
/// <remarks>
/// Edge cases:
/// - Boundary values (650 credit, 0.43 DTI) should approve.
/// - Missing or null values should throw an exception.
/// </remarks>
public bool EvaluateLoanApproval(int creditScore, double debtToIncomeRatio)
{
// Implementation here
}
The method signature and meaningful comments become the source of truth. AI uses this to generate both implementation and tests. The order—tests-first or code-first—becomes irrelevant.
When TDD Still Helps
TDD isn’t dead. Some scenarios still benefit from writing tests manually:
- Ambiguous domains – When business logic is complex, tests reduce misunderstandings better than comments alone.
- Legacy refactoring – Tests-first provides a safety net that specifications may not capture.
- API exploration – Tests clarify interface design before implementation.
For many greenfield projects with well-defined requirements, clear specifications are enough.
The Real Lesson of TDD
Most developers miss that TDD has two distinct aspects:
- The goal: Clear, testable specifications that drive quality code.
- The mechanics: Failing test → implementation → refactor.
AI doesn’t make the goal obsolete. It makes the mechanics optional. High-quality codebases always share one trait: clarity, unambiguity, and testability. Previously, tests provided that clarity. Now, detailed specs can do the same—faster and often more efficiently.
Adapting Your Workflow
With AI in the picture, my workflow has evolved:
Before (Classical TDD):
- Write a failing test.
- Implement minimal code.
- Refactor.
- Repeat.
Now (Spec-Driven Development):
- Write a clear method signature with detailed comments.
- Let AI generate the implementation and tests.
- Review and refine both.
- Ship.
This new workflow is faster and more efficient—but only if you put the same rigor into specifications as you did into tests.
If I’m lazy with specs, AI will produce mediocre code and incomplete tests. But when I provide clear, comprehensive specifications, AI generates production-quality code with better test coverage than I could manually write.
Final Thoughts
AI hasn’t rendered TDD obsolete; it’s simply highlighted what truly matters: the clarity of specifications. The mechanical steps of TDD are now optional, but the need for clear, testable specifications remains critical.
In the AI era, we’re simply shifting our focus from mechanical processes to thoughtful, detailed design.
Watch the Full Discussion
I dive deeper into this topic with coding examples and demonstrations. If you want to see spec-driven development in action alongside classical TDD, check out the full demo video: