Building AI-Powered Search: Beyond Basic Vector Similarity

Vector similarity search was the first step. Production AI search systems need much more to handle real user queries effectively. Here's what we've learned building search for millions of queries.

Hybrid search combines vector and keyword approaches. Pure vector search misses exact matches that matter—product SKUs, error codes, specific names. Pure keyword search misses semantic connections. Combine them with learned weighting that adjusts based on query type.

Query understanding transforms what users type into what they mean. "cheap laptops under 500" should understand "cheap" is relative, "laptops" might include notebooks, and "500" is dollars unless specified otherwise. LLMs excel at this query expansion and normalization.

Ranking goes beyond similarity scores. User behavior signals (what people click, what they buy) should influence ranking. Freshness matters for some queries and not others. Personalization tailors results to individual patterns.