Testing AI systems: a practical guide for engineering teams

AI systems break the core assumption of traditional QA: that identical inputs produce identical outputs. Because AI behavior is probabilistic and context-dependent, testing shifts from binary pass/fail assertions to scoring outputs on relevance, accuracy, and safety. Effective AI testing also spans model-level and system-level evaluation, requires continuous post-deployment monitoring, and can't fully replace human judgment.