How would you improve AI search quality?

Signal to interviewer

I can raise AI search quality through root-cause decomposition and prioritized execution, not generic model tweaking.

Clarify

Define search context: enterprise knowledge, in-product support, or broad discovery. Segment query intents into navigational, exploratory, and task-completion classes. Agree on quality objective: correctness, actionability, coverage, and speed.

Approach

Build a failure taxonomy, map each class to system causes, and prioritize by user harm and frequency. Assign owner and verification plan for each failure class. Ship targeted fixes across retrieval, ranking, grounding, and answer generation.

Metrics & instrumentation

Primary metric: successful task completion following search. Secondary metrics: reformulation reduction, citation utility, and clarification burden. Guardrails: misinformation reports, confidence miscalibration, and latency regressions. Instrumentation: intent labels, retrieval diagnostics, source freshness, and feedback linked to failure taxonomy.

Tradeoffs

Stronger grounding improves trust but can increase latency. Tighter filtering reduces hallucinations but may hurt exploratory recall. Personalization lifts relevance but can weaken cold-start consistency.

Risks & mitigations

Risk: treating symptoms, not causes; mitigate with diagnostic dashboards and owner accountability. Risk: benchmark overfitting; mitigate with live traffic cohorts and rotating eval sets. Risk: metric gaming; mitigate with task-outcome primary metric and review cadence.

Example

For policy search, if answers are plausible but outdated, add freshness-aware retrieval weighting, recency filters, and citation timestamps in the UI.

90-second version

Use failure mode analysis end to end: classify failures, map roots, prioritize by impact and frequency, ship targeted fixes, and verify against task completion and trust guardrails.