You launched an AI assistant. Do you really know how it's performing?

AI assistants require evaluation at three levels: individual turns, full sessions, and cohort trends. A framework combines evaluation (defining quality metrics), observability (capturing system behavior), and traceability (connecting failures to causes). Quality assessment uses CORE dimensions (clarity, relevance, tone, accuracy) plus CUSTOM product-specific metrics, weighted by importance. Hybrid review pipelines combine manual calibration with automated LLM-as-judge scaling. Success requires instrumenting telemetry across model, retrieval, and tooling layers, then connecting conversation quality to business outcomes like retention and deflection.