Understanding LLM Observability: A Practical Guide

Observability in traditional software is well-understood: metrics, logs, traces. But when your system’s core logic is a large language model, the rules change.

Why LLM Observability is Different

Traditional APM tools can tell you that a request took 3.2 seconds. They can’t tell you why the model hallucinated or whether the retrieval step returned relevant context. LLM observability bridges that gap.

The Three Pillars for AI Systems

1. Trace-level visibility — Every chain invocation, every retrieval call, every model interaction logged with input/output pairs
2. Quality signals — Automated evaluation of response relevance, groundedness, and coherence
3. Cost attribution — Token-level cost tracking per user, per feature, per model

More on this in upcoming posts.