After a courts corpus refresh, continuous monitoring shows a spike in low-groundedness responses. What is the appropriate ops interpretation?

Investigating content or retrieval changes from the corpus refresh that harmed grounding is correct because continuous monitoring exists to surface quality regressions after production changes. A spike in low-groundedness responses immediately after a courts corpus refresh strongly implicates document ingestion, chunking, metadata, or index behavior rather than groundedness scoring variance, so recalibrating thresholds would hide a real regression instead of repairing it. Ops should treat the monitoring signal as actionable: compare pre/post retrieval samples, inspect new or altered court documents, and repair grounding before promoting further model changes.

Investigating groundedness scoring variance to recalibrate thresholds after the corpus refresh mistakes a likely real quality drop for metric noise; a spike that coincides with a known content change should be validated against retrieval samples first, not normalized away by moving thresholds. Investigating system prompt citation compliance as the cause of the post-refresh groundedness decline misplaces the timing; the prompt did not change in the refresh window, so a sudden spike points to altered court documents or retrieval behavior that citation instructions alone cannot fix. Investigating foundation model behavior shifts causing groundedness loss after the corpus refresh ignores that no model change occurred in this window; chasing model drift risks delaying inspection of the known content and retrieval changes that best explain the timing.

Exam caveat: correlation of monitoring regressions with recent content or retrieval changes is the GenAIOps interpretation. Corpus refresh + groundedness spike → investigate retrieval/content path first, do not disable monitoring or blindly retrain.

Operational check: freeze further unrelated promotions, sample low-groundedness traces from the spike window, diff corpus and index artifacts from the refresh, restore or re-chunk problem documents, re-run groundedness evals and live monitoring until rates return to baseline, then document the change in the courts GenAI runbook.