What Is Vector Search?
Vector search finds relevant content by comparing its underlying meaning rather than matching exact words, using AI-generated numerical representations of text (called embeddings) that place similar concepts close together in mathematical space.
How It Differs from Keyword Search
Traditional keyword search matches the literal words in a query against the words on a page, using techniques like stemming to catch close variations. Vector search instead compares meaning: a query about “affordable running shoes” can match content about “budget-friendly trainers” even though they share almost no exact words, because the underlying concepts are recognised as closely related.
Why It Matters for AI-Driven Search
Vector search underpins how most modern AI search and retrieval systems (including RAG) find relevant source material to ground their answers — making genuinely comprehensive, clearly written content that covers a topic’s full conceptual range more valuable than content narrowly optimised around exact keyword phrases.
