LLM Evaluation and Observability Questions

Measuring, monitoring and releasing generative and LLM-powered systems. Covers evaluation of open-ended outputs (human review, calibrated model-graded judges, reference-based and embedding scores, perplexity), hallucination, toxicity, privacy-leak and red-team checks, offline benchmarks versus online monitoring, and production observability (logs, traces, metrics, cost and token attribution, privacy-safe telemetry, drift and quality alerts). Also release decisions: A/B tests, canary rollouts, SLOs and go or no-go gates. Building the grounding architecture itself, calibration of predictive models and general software testing are covered elsewhere.