LLM observability is the practice of instrumenting, monitoring, and analyzing model behavior in production. LLM observability gives you the telemetry you need to find regressions, debug failures, and measure real-world impact. Good LLM observability feeds LLM evaluation and drives continuous improvement.
Start LLM observability by logging prompts, model versions, outputs, latencies, and confidence signals. Create dashboards for error rates, hallucination incidents, and policy violations. Use sampling for human review and add anomaly detection to catch distribution drift. LLM observability also connects to agent engineering—understanding the agent’s decisions requires good traces and context.
Make LLM observability privacy-aware: mask PII, and store only what’s necessary for debugging. Correlate LLM observability signals with product metrics so you can see business impact. Use the observability data to prioritize Prompt Optimization and AI Guardrails work; this closes the loop between detection and remediation.
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