How would you improve AI accuracy in production?

### Signal to interviewer

I can improve AI accuracy systematically by tying quality work to production telemetry and root-cause ownership.

### Clarify

I would clarify what accuracy means for the product, which cohorts are highest risk, and acceptable latency/cost impact.

### Approach

Run a production error reduction loop: detect failures, classify root causes, ship targeted fixes, and validate with shadow/live checks.

### Metrics & instrumentation

Primary metric: verified correctness rate in production samples. Secondary metrics: error recurrence, correction turnaround time, and confidence calibration quality. Guardrails: latency drift, over-refusal rate, and unresolved severe failure buckets.

### Tradeoffs

Heavier validation increases correctness but adds latency and spend. Faster response paths improve UX but can miss subtle errors.

### Risks & mitigations

Risk: noisy feedback signals; mitigate with weighted labeling. Risk: overfitting to known errors; mitigate with rotating eval sets. Risk: ownership gaps; mitigate with root-cause SLAs.

### Example

In a policy assistant, retrieval-related inaccuracies are tracked as a separate class with dedicated freshness and citation fixes.

### 90-second version

Improve production accuracy through a root-cause loop, not one-off tuning. Measure verified correctness, assign ownership by error class, and balance validation depth with latency constraints.