Cosine similarity is a way of measuring, in vector search, how closely two texts correspond in meaning. Each text is converted into a numerical representation, and the direction of these representations is compared – the closer they are, the higher the similarity. In practice, this means the system does not look for matching words but for close meaning, so a paragraph about reducing energy costs will also be found for a query about saving on electricity. Two conclusions follow for content creation. First: a text focused on a single topic has a clearer representation than a paragraph mixing three topics, which ends up meaning nowhere in particular. Second: overly short fragments without context have an unreliable representation, so a paragraph should contain the complete claim, including the naming of the topic.
See also: Content chunking, Hybrid search, Top-k retrieval.
