Semantic search
Semantic search is a way of finding information based on meaning rather than exact wording, so a query like “can I bring my dog?” can match content that says “pets are welcome” even with no shared keywords.
How it works
Traditional keyword search fails when people phrase things differently from your content. Semantic search compares the meaning of the query and the content — using embeddings — so it still finds the right match.
The best systems combine both: semantic matching for meaning, plus keyword matching for exact terms like product names, to get the strengths of each.
How SiteMind uses it
SiteMind uses semantic search to match visitor questions to your content by meaning, and can blend it with keyword and exact-term matching so it handles both natural phrasing and specific names well.
Related terms
Embeddings
Embeddings turn text into lists of numbers that capture its meaning, so a computer can tell which pieces of content are about the same thing.
Vector database
A vector database stores content as embeddings and can instantly find the pieces whose meaning is closest to a query.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a method where an AI looks up relevant information from a trusted source before answering, so its replies are grounded in facts instead of guesses.
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