LLM Observability
LLM Observability explained
A wrong answer may originate in several places: a missing source, incomplete tool data or an unjustified model conclusion. Connected trace data help narrow down the possibilities.
Measurements and quality judgements should be connected but remain distinct. A successful call can return a wrong answer; low latency says little about clarity. Supplement technical observations with expert sampling or suitable evaluations.
Decide which information is actually needed. Prompts, answers and tool data may contain confidential material. Access permissions, limited retention and appropriate reduction of recorded content belong in the operational design.
The team needs a path from signal to response: who investigates, when should a workflow pause and how is a correction verified? A dashboard alone does not improve the application.
Examples
Hypothetical application
A product adviser answers using outdated data. Its trace shows which data version was loaded. The team corrects the update step and tests comparable questions again. The trace guides investigation; substantive comparison confirms the correction.
Key Points
- Traces reveal processes, not truth by themselves.
- Connect technical measurement with substantive assessment.
- Collect only needed content with appropriate access.
- Assign investigation and correction responsibilities.
Practical application
Choose a specific failure or operational question. Identify the trace data needed to investigate it and collect those with appropriate safeguards. Connect findings to a verifiable improvement.
Useful measures
Investigable failures
Incidents for which relevant workflow steps can be traced.
Latency and consumption
Technical effort per defined task and version.
Time to verified correction
Duration from the reported failure to a checked result.
Common mistakes
- Assuming a successful API call means a correct answer.
- Recording full content without a clear purpose.
- Collecting metrics without acting on findings.
Sources and context
- OpenTelemetry: GenAI agent spans
Conventions for trace data and sensitivity of recorded content; not automatic fact-checking.
Frequently Asked Questions about LLM Observability
No. A log initially shows what occurred in the recorded workflow. Establishing truth requires a substantive comparison or a suitable additional check.
No. Choose the data required for the purpose. Full content can contain sensitive information; reduced records may suffice for a particular investigation.
Observability makes operational workflows inspectable. Evals assess results against tasks and criteria. They can be connected but are not the same.
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