Observability for LLM apps is the practice of collecting structured logs, traces, metrics, and feedback so you can see what an AI application actually did, why it behaved that way, and where it failed.
LLM apps are harder to debug than traditional software because the “logic” is partly produced at runtime by a model. A user sees a bad answer, but the cause could be the prompt, retrieved context, tool output, model behavior, latency, token limits, or a broken chain of steps.
Observability helps you:
In practice, most teams start with tracing because it gives the clearest end-to-end view of one request.
The core idea is to record each important step of an LLM request as a trace. A trace is a timeline of spans or events: the user input, prompt construction, retrieval calls, model calls, tool calls, output parsing, and final response.
Each span usually includes metadata such as:
A good observability setup also links traces to higher-level metrics and evaluations. Metrics answer “how often” or “how much” across many requests; traces answer “what happened” for one request. Evaluations and feedback add a quality signal, such as a human label or an automated judge score.
For LLM apps, tracing is especially useful because the system is often a pipeline: prompt → retrieval → model → tool use → post-processing. The trace lets you inspect the whole chain instead of guessing from the final answer alone.
A user asks: “Summarize our refund policy for enterprise customers.”
A trace might show:
If the answer is wrong, the trace may reveal that the retriever fetched a consumer policy instead of the enterprise one, or that the model ignored the right context.
