What is a Semantic Search?

Traditional keyword search matches literal terms — a query for "cancel subscription" won't reliably surface a document titled "how to end your plan" unless those exact words appear. Semantic search instead converts both the query and the candidate documents into vector embeddings — numerical representations that place conceptually similar text close together in a high-dimensional space — and retrieves the nearest neighbors regardless of exact wording. This is what makes modern AI assistants and support tools able to answer a paraphrased question by finding the right documentation even when the user's phrasing doesn't match it. It's implemented with an embedding model plus a vector database or index that supports fast nearest-neighbor lookup at scale, since brute-force comparison against every document doesn't scale to large corpora. Semantic search is often combined with traditional keyword search in a hybrid approach, because pure semantic matching can miss cases where an exact term (a product SKU, an error code) genuinely matters and shouldn't be fuzzed away by conceptual similarity.