Testable Design for AI Systems

AI systems are hard to test because models are non-deterministic and call external services, so testability has to be designed in: isolate the non-deterministic model behind an interface so you can mock it, separate deterministic logic (parsing, retrieval, formatting) from the model call and test it normally, and assert on metric tolerances rather than exact outputs. Applied-AI interviews probe it because untestable LLM code regresses silently, and the discipline of mocking the model and testing the deterministic parts is what keeps a system reliable.