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Semantic Search

Semantic search matches on meaning rather than wording, so a document can come back as relevant without sharing a single word with the question.

Keyword search is precise and literal: it finds what you spelled. That fails constantly inside a company, where the same thing is an SOW to one team, a scope doc to another and "the Acme agreement" in the thread where it was actually decided. Semantic search compares embeddings instead of strings, so the wording stops mattering.

It is not a replacement for keyword matching, and production systems generally run both. Semantic retrieval is weakest exactly where literal matching is strongest — error codes, ticket numbers, product SKUs, anything where the exact string is the point. The useful system is the one that can do both and choose.

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Frequently Asked Questions

Vector search is the mechanism and semantic search is what it delivers. In practice the two terms are used for each other.

Because it wins on exact identifiers and rare strings. Hybrid retrieval, running both and merging the results, beats either alone on real company corpora.

Only if the system applies them, ideally when the answer is produced rather than when the content was indexed. The retrieval method itself enforces nothing.