LLM Observability
You cannot improve what you cannot see. LLM observability means instrumenting every model call so you know what was sent, what came back, how long it took, and what it cost - and then using that data to catch regressions, attribute spend, and prove ROI. This section covers the tools and patterns for making your LLM application transparent.
In This Section
Tracing LLM Calls in Production
OpenTelemetry for LLMs, LangSmith, Helicone, Arize Phoenix - what spans to create, what metadata to attach, and how to query traces.
Logging Prompts & Responses Safely
What to log, what to redact, how to handle PII in prompt logs, structured logging formats, and retention policies.
Cost Attribution & Token Dashboards
Tracking token spend by feature, user, or team - chargeback patterns, budget alerts, and the metrics that predict your next invoice.