Vector Search in Production: Semantic Retrieval from Principles to Deployment
Vector search in production: map queries and docs into a semantic space, replace brute-force with ANN, pick HNSW or IVF+PQ with hybrid retrieval plus reranking.
Vector search in production: map queries and docs into a semantic space, replace brute-force with ANN, pick HNSW or IVF+PQ with hybrid retrieval plus reranking.
Site search UX optimization links autocomplete, correction, synonyms, faceted filters and zero-result recovery as one loop via latency and zero-result rate.
Search ROI is hard to measure: a three-layer usage-quality-revenue measurement, contribution vs incremental conversion, an ROI formula, and four misjudgments.
Enterprise knowledge base search fails when documents scatter and permissions differ. Rollout: ingestion, permission alignment, metadata, ranking, governance.
Recommendation systems need core metrics: CTR for appeal, CVR for value, diversity and novelty vs drift, coverage for long-tail; offline iterates, online A/B.
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