Diagnose a sudden drop in content quality for AI meeting assistant

Problem Statement Description

Restaurant owners use an AI meeting assistant to capture discussions from staff meetings, supplier calls, franchise check-ins, hiring interviews, and operational reviews, then turn them into summaries, action items, decisions, and follow-ups. Recently, the perceived quality of this generated content has suddenly dropped for this segment, creating risk that owners miss key operational details or lose trust in the assistant.

Your task is to diagnose the issue before recommending fixes. Treat this as a root-cause analysis problem: clarify what “content quality” means, determine whether the drop is real or measurement-driven, isolate where in the workflow the degradation occurs, and identify the most likely contributing factors across product, model, data, user behavior, and external conditions.

Assume restaurant owners may operate in noisy environments, use domain-specific language, discuss shifts, menus, inventory, vendors, reservations, staffing, and compliance issues, and may rely on the assistant during time-sensitive business operations. The investigation should be structured enough to support immediate triage as well as longer-term prevention.

The experience should consider:

- How to frame the anomaly: timing, magnitude, affected quality dimensions, baseline, and whether the drop is sudden versus gradual.

- Which segments to compare, such as restaurant type, geography, device, meeting length, language/accent, noise level, plan tier, and new versus existing users.

- Instrumentation checks, including changes in scoring logic, feedback collection, transcription confidence, model versioning, question/config changes, and data pipeline completeness.

- Workflow breakpoints across audio capture, speech-to-text, diarization, summarization, action-item extraction, formatting, and delivery.

- Hypotheses involving product releases, model regressions, restaurant-specific vocabulary, environmental audio, user behavior changes, integrations, or third-party dependencies.

- Evidence needed to validate or reject hypotheses, including logs, user feedback, sampled outputs, human review, cohort analysis, and before/after comparisons.

- Near-term mitigation options to protect affected users while diagnosis continues, without jumping prematurely to a permanent fix.

- Prevention mechanisms such as monitoring, alerting, quality guardrails, rollout controls, and segment-specific evaluation sets.

The goal is to demonstrate a clear, prioritized RCA approach that separates symptoms from causes, uses evidence rather than assumptions, and leads to a confident understanding of why content quality declined for restaurant owners before proposing any remediation plan.