A vector database is a system that stores embeddings, the numerical representations of text, and retrieves the most similar ones quickly. When an AI system needs to find the passages most relevant to a query, it converts the query into an embedding and asks the vector database for the closest matches.
For brands, vector databases are the retrieval engine behind much of AI search and RAG systems. The detail is technical and not something you act on directly, but the implication is the same as for embeddings: content is retrieved by meaning, so clear, well-scoped, well-structured passages are easier to match and surface than padded or unfocused ones.