Semantic Search as a Product: What Changes When Retrieval Understands Intent
· 11 min read
Most teams building semantic search start from a RAG proof-of-concept: chunk documents, embed them, store vectors, query with cosine similarity. It works well enough in demos. Then they ship it to users, and half the queries fail in ways that have nothing to do with retrieval quality.
The reason is that RAG and user-facing semantic search are solving different problems. RAG asks "given a question, retrieve context for an LLM to answer it." Semantic search asks "given a user's query, surface results that match what they actually want." The second problem has a layer of complexity that RAG benchmarks systematically ignore — and that complexity lives almost entirely before retrieval begins.
