AI Observability: Log What Helps You Debug a Failure
When an AI feature fails, the final answer rarely explains the whole problem. You may need to inspect retrieval, tool calls, model selection and retries. Design observability around questions you need to answer, while minimising the private content captured in routine logs.
Why it matters
More logging is not automatically better. Large transcripts can be difficult to search and can create a sensitive data store. Structured events often reveal the failure more clearly than an unfiltered conversation archive.
A practical workflow
- Assign a request identifier and record the feature, model version, timing and success or failure outcome.
- Capture retrieval document identifiers and tool status without storing secrets or unnecessary personal fields.
- Track retries, usage and review outcomes so a successful-looking answer does not hide an expensive failure path.
- Define access, retention and deletion rules for traces before collecting production data at scale.
An example
If a handbook answer cites an old policy, a trace should reveal which document version was retrieved. That is more useful than only logging 'answer generated'. If retrieval was current but the answer was wrong, the investigation can move to the generation step.
A mistake to avoid
Do not send full customer conversations to an observability service by default. Check what the integration records, redact sensitive fields and confirm that logging settings match your organisation's data-handling requirements.
Try it and check the result
Create a known retrieval error and follow it from the user-visible answer back to the relevant event. If the trace cannot identify the failing stage, improve the event structure before adding more raw text.
Reference and further reading
NIST AI Risk Management Framework.
The workflows and examples in this guide are illustrative. Product controls and availability can change; check the linked official guidance for your setup.
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