Traditional application monitoring tells you that an LLM request succeeded, not whether the result was useful, grounded, safe, or unnecessarily expensive. Without deeper visibility, teams discover quality regressions through frustrated users and rising invoices.
LLMOps observability connects model behavior to the surrounding product workflow. This guide covers the traces, quality signals, cost controls, and privacy decisions needed to diagnose failures without collecting sensitive data indiscriminately.
Trace the complete workflow
Record the request path across retrieval, prompt assembly, model calls, tool execution, validation, and final delivery. Include model, prompt, dataset, and application versions. A shared trace identifier lets engineers distinguish model latency from slow search, external APIs, or approval queues.
Measure outcomes, not tokens alone
Track latency, token usage, retries, tool failures, refusals, and cost alongside task-specific outcomes such as accepted suggestions, resolved cases, or human overrides. Segment by workflow and model version; fleet-wide averages conceal expensive or unreliable edge cases.
Capture quality safely
Use structured user feedback, sampled expert review, and evaluation jobs based on real failure patterns. Redact secrets and personal data before logging, restrict access, and define retention periods. In regulated workflows, store references or hashes when raw prompt content is unnecessary.
Make telemetry actionable
Create alerts for sudden cost, latency, error, or quality shifts and link dashboards to deploy versions. Establish who investigates each class of signal. Begin with one high-value workflow, instrument every stage, and use observed failure categories to decide which additional metrics deserve ongoing operational ownership.