How would you monitor AI performance and detect regressions?

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ANSWER MODE

WRITTEN ANSWER

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### Signal to interviewer

I can design regression detection that is user-impact aware and operationally actionable.

### Clarify

I would clarify which outcomes define performance, how often systems change, and what rollback latency is acceptable.

### Approach

Build a regression sentinel stack: live telemetry, scheduled canary evals, and change-linked diff analysis for every model or prompt update.

### Metrics & instrumentation

Primary metric: task success stability index by journey. Secondary metrics: canary drift score, complaint conversion after responses, and anomaly detection precision. Guardrails: high-severity incident spikes, safety-policy breach growth, and delayed rollback.

### Tradeoffs

Lower alert thresholds increase recall but create alert fatigue. Higher thresholds reduce noise but can miss early regressions.

### Risks & mitigations

Risk: hidden regressions in niche flows; mitigate with stratified canaries. Risk: metric gaming; mitigate with multi-metric dashboards. Risk: rollback hesitation; mitigate with pre-approved rollback playbooks.

### Example

In an enterprise assistant, each retrieval update triggers canary replay across legal, finance, and HR tasks before full release.

### 90-second version

Monitor AI with user-centered signals plus change-aware canaries. Detect meaningful regressions quickly, route to owners, and make rollback a standard fast path.

FOLLOW-UPS

Clarification

Which user journey should anchor the primary regression metric?
How quickly must severe regressions be detected and rolled back?

Depth

How would you stratify canaries to cover long-tail workflows?
What data model links regressions back to exact change events?

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