Monitoring LLM behavior: Drift, retries, and refusal patterns

The continuous improvement workflow (I have modified what’s in the article because it was incomplete - didn’t include the taxonomization step, etc.)

Capture: A user triggers an explicit negative signal (a “thumbs down”) or an implicit behavioral flag in production.

Triage: The specific session log is automatically flagged and routed for human review.

Root-cause analysis and annotation: A domain expert investigates the failure and identifies the gap. It is annotated with a natural language label identifying the type of failure. May require escalation to SME in some cases.

Error taxonomization: Labels from the annotation are used to construct hierarchical error taxonomy, surfacing key areas to focus attention

Dataset augmentation: The novel user input, paired with the newly corrected expected output, is appended to the offline Golden Dataset alongside several synthetic variations. This may require SME input.

Eval creation: Based on error taxonomy and produced ground truth data,

Regression testing: The model is continuously re-evaluated against this newly discovered edge case in all future runs.