Large Language Model Observability and Evaluation Questions

Covers the end to end product and technical considerations for monitoring, evaluating, and troubleshooting large language model systems. Topics include what observability means for model driven features, which signals to capture such as input provenance, token usage, latency, error modes, and outcome quality, and how to design instrumentation and data contracts that ensure consistent and auditable telemetry. It includes evaluation approaches and metrics such as relevance, accuracy, hallucination rate, calibration, and cost, and the trade offs between human labeling, automated metrics, and model driven judges. Product design aspects cover dashboards, alerts, logging, tracing, debugging interfaces, and developer workflows that make investigation and root cause analysis efficient. Finally this topic addresses operational concerns for an observability platform including storage and cost trade offs, scaling telemetry pipelines, privacy and compliance constraints, and how evaluation and observability feed back into model improvement cycles.