Finding the most relevant content by comparing meaning-carrying vectors, ranking by nearest neighbour rather than keyword overlap.
Vector search finds the most relevant content by comparing vector embeddings — dense numeric representations of meaning — and returning the nearest neighbours, rather than matching keywords. Two passages that mean the same thing sit close together in vector space even if they share no words.
Relevance is scored with a similarity measure, typically cosine similarity or, equivalently for normalised vectors, a dot product. This is the "retrieve" step behind retrieval-augmented generation and grounded AI answers.
Our own benchmarking showed how practical the trade-offs are: truncating 1,024-dimensional embeddings to 256 via Matryoshka learning roughly halved processing time while holding accuracy at 99.5%. Efficient vector search — and the encoding choices behind it — is a foundation of modern relevance engineering.
