Vector Search

Vector search converts text into embeddings — long lists of numbers capturing meaning — and retrieves content whose vectors sit closest to the query's. It's the machinery that lets an engine match 'tool to check if ChatGPT mentions my brand' with a page that never uses those words.

It's the technical layer beneath semantic search and most RAG pipelines: retrieval happens in vector space first, then the model reads the winners. The practical takeaway for content is to cover concepts clearly and completely — synonyms and related sub-topics — rather than repeating one exact phrase.