AI

Traditional monitoring tells you whether a request succeeded. It has nothing to say about whether the answer was actually correct or useful, and for an AI-powered feature that's often the question that matters most. This course covers why AI systems need a genuinely different observability practice, how to build evaluation sets that catch quality regressions before users do, the difference between offline evaluation and live production monitoring, and how to catch the specific failure mode where a model is confidently, fluently, and completely wrong.