Semantic Search
Also known as: meaning-based search, vector search
Semantic search is a search approach that interprets the meaning and intent behind a query — often using embeddings and language models — rather than relying solely on exact keyword matches.
In simple terms
Older search matched the literal words in a query to the words on a page. Semantic search instead tries to understand what the user means, so it can return relevant results even when the wording differs.
It typically represents text as vectors (numeric embeddings) so that conceptually similar content sits close together, letting the system match on meaning rather than characters.
Why it matters
Semantic search underpins most modern AI discovery. It rewards content that clearly expresses concepts and relationships, not content stuffed with exact-match keywords.
For brands, it means being understood conceptually — covering a topic thoroughly and unambiguously matters more than repeating a target phrase.
How it works
Content and queries are converted into embeddings that capture meaning. The system compares them to find the closest matches, then ranks or retrieves the most relevant passages.
Because matching is based on meaning, comprehensive and clearly written content is easier to retrieve for a wide range of related queries.
Example
A page about 'reducing customer churn' can rank for 'how to stop users from canceling' under semantic search, because the system recognizes the two express the same intent — even with no shared keywords.
Frequently asked questions
Do keywords still matter with semantic search?
They matter less as exact strings and more as topic signals. Covering a concept clearly and completely is more effective than repeating a single phrase.
Is semantic search the same as vector search?
Vector search is a common technique used to implement semantic search. Semantic search is the broader goal of matching meaning; vectors are one way to achieve it.
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Related terms
Last updated August 29, 2026