Semantic Search

Semantic search understands intent and meaning instead of matching literal strings. By converting text into numerical vectors, called embeddings, it can retrieve passages that are conceptually related to a query even when they share few keywords. This is the retrieval backbone behind most modern answer engines and RAG systems.

For AI visibility, semantic search means your content is found based on how well it actually answers the meaning of a question, not on keyword density. Writing clear, self-contained passages that directly and completely address likely questions makes them easier to retrieve semantically and quote.

DeepLexa's best practices, clean, answer-first passages that fully address a question, are precisely what semantic retrieval rewards.