ML-Powered Search & RankingQuery Understanding (Intent, Parsing, Rewriting)Hard⏱️ ~3 min

Failure Modes and Production Guardrails

Over-Correction and User Frustration

Aggressive spell correction changes valid queries. "Golang" corrects to "goal" in a general-purpose search. Brand names, technical terms, and proper nouns are especially vulnerable. Users who repeatedly see wrong corrections lose trust. Prevention: maintain allowlists of valid terms by domain, require higher confidence for corrections on capitalized words, and always provide "search instead for [original]" option.

Expansion Drift

Query expansion adds semantically similar but contextually wrong terms. "Python book" expands to include "snake" in a programming context. "Apple stock" expands to "fruit" when user wants financial data. Drift compounds: each expansion term can trigger further expansions. Prevention: use entity linking to constrain expansions ("Python" linked to programming language excludes animal expansions), limit expansion depth, validate expansions against user click patterns.

Intent Misclassification Cascades

Wrong intent routes to wrong backend, returning completely irrelevant results. A transactional query hitting an informational index returns articles instead of products. User abandons search. Unlike ranking errors (wrong order of good results), intent errors return the wrong result set entirely. Mitigation: use confidence thresholds for routing; if intent classifier is uncertain (<70% confidence), query multiple backends and blend results.

⚠️ Guardrail: Monitor query understanding changes through A/B tests before full rollout. Track: zero-result rate, click-through rate, search abandonment, "did you mean" acceptance rate. Regressions indicate failure modes.
💡 Key Takeaways
Over-correction changes valid queries (Golang → goal); use allowlists and confidence thresholds
Always provide "search instead for [original]" option to recover from bad corrections
Expansion drift: Python (programming) expands to snake; use entity linking to constrain
Intent misclassification returns wrong result set entirely, not just wrong order
Route to multiple backends when intent confidence < 70%; blend results
📌 Interview Tips
1Describe over-correction problem with technical terms (Golang, brand names) and allowlist solution
2Explain expansion drift with Python example and entity-linking constraint
3Recommend multi-backend routing when intent confidence is low
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