What is Semantic Search?
RAG & RetrievalSearch by meaning rather than literal terms: the query 'contract termination window' finds 'notice period' without sharing a word. The umbrella term for what embeddings make possible.
Why It Matters
Users do not know your vocabulary. They search for “how do I cancel” when the document says “termination procedure,” for “refund timeline” when the policy says “reimbursement window.” Keyword search returns nothing; semantic search returns the right passage, because it matches intent rather than spelling.
It is the difference between a search feature and an answer system, and the foundation every RAG pipeline is built on.
How It Works
Documents and queries both get mapped into the same meaning-space by an embedding model. Retrieval becomes geometry: the passages nearest the query’s vector are the semantically closest ones, surfaced by vector search.
In production, semantic search rarely runs alone. Hybrid search pairs it with keyword matching so exact identifiers survive, and a reranker applies the precision pass over the combined candidates.
Where It Breaks
Semantic search fails where meaning is not enough: part numbers, legal citations, error codes, and proper nouns need exactness that meaning-space does not have. It also inherits the embedding model’s domain blind spots: distinctions the model cannot draw are distinctions the search cannot make.
The subtler failure is false confidence. A semantically adjacent wrong answer looks identical to a right one in the result list, and without citation-strict generation downstream, nobody notices the substitution.
How Flytebit Handles It
Semantic retrieval in our RAG builds is one leg of the hybrid pair, never the whole system, and the end-to-end path is evaluated on real user questions, so a query that needs exact matching cannot silently degrade to vibes-based similarity.