A vector database stores content as embeddings — long lists of numbers that capture meaning — and vector search retrieves items by mathematical closeness in that space rather than exact keyword matches. Together they let software find things that mean the same thing, even when the words differ.
Keyword search looks for the characters you typed. If a shopper searches "affordable laptop" but your product copy only says "budget notebook computer," a literal index may miss it. Vector search converts both phrases into embeddings and compares their positions: because the meanings are close, the match still surfaces. That shift from matching strings to matching meaning is what makes results feel intelligent, and it underpins semantic search.
When an AI assistant answers a question about your business, it often doesn't rely on memory alone — it retrieves relevant passages first, then writes from them. That retrieval step usually runs against a vector database. The pattern has a name, retrieval-augmented generation, and it's why clearly written, well-structured content now needs to be retrievable by meaning, not just crawlable by keyword.
Optimising to be surfaced by these systems is the heart of generative engine optimization.