Test-Driven Development (TDD)
A software development approach where tests are written before the implementation code, following a red-green-refactor cycle that ensures every feature has automated test coverage from the start.
TDD follows a strict cycle. First, write a failing test that describes the desired behavior (red). Then write the minimum code to make the test pass (green). Finally, refactor the code to improve its design while keeping tests passing (refactor). This cycle repeats for each small increment of functionality, building up comprehensive test coverage organically.
The benefits go beyond test coverage. TDD forces developers to think about the interface before the implementation, leading to cleaner APIs and better separation of concerns. The immediate feedback loop catches design issues early. And the resulting test suite serves as living documentation of the system's expected behavior.
For AI applications, TDD applies well to the non-model layers: API contracts, data transformation logic, input validation, output formatting, and integration code. Testing the AI components themselves requires a different approach, using evaluation datasets and statistical assertions rather than deterministic equality checks. A common pattern is TDD for the application shell with property-based and evaluation-based testing for the AI components.
Related Terms
A/B Testing
A controlled experiment comparing two or more variants to determine which performs better on a defined metric, using statistical methods to ensure reliable results.
Feature Flag
A software mechanism that enables or disables features at runtime without deploying new code, used for gradual rollouts, A/B testing, and targeting specific user segments.
MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords, typically powered by embedding-based similarity comparison.
CI/CD (Continuous Integration / Continuous Deployment)
An automated software practice where code changes are continuously integrated into a shared repository, tested, and deployed to production, reducing manual intervention and accelerating delivery cycles.