What algorithms are used for vector search relevance?

Vector search algorithms find content by comparing embeddings, which represent meaning in a numerical space. Common approaches include approximate nearest neighbor methods such as HNSW, IVF, and product quantization, often paired with reranking or hybrid retrieval. These algorithms matter for AI search because they influence which passages are retrieved before an AI assistant writes an answer. Content teams do not need to tune the algorithm directly, but they should understand its implication: semantically clear, focused, entity-rich content is easier to match. Pages should answer real prompts, avoid ambiguity, and include enough context for embeddings to represent the topic accurately.