LLM Observability Explained
What is LLM observability
LLM observability is the practice of tracking model behavior across prompts, context, tools, and outputs to understand how decisions were made.
It combines traces, performance signals, and error visibility into one operational view.
Why it matters
Without observability, AI reliability work is reactive and slow. Teams see bad outputs but cannot prove why they happened.
Observability reduces mean time to detect and mean time to resolution for AI incidents.
Key metrics (latency, drift, errors)
Track latency by step, quality drift over time, tool-call error rates, and token usage patterns.
These metrics reveal where performance degrades before user impact becomes severe.
Observability vs logging
Logging records isolated events. Observability connects those events into causal chains with context.
For AI systems, that difference is critical because failures often emerge from interaction effects across many steps.