Vector Databases

A vector database stores content as embeddings — numerical representations of meaning — so that searches can find results similar in concept rather than just in exact words. Ask a question about "employee termination policy" and a vector database returns documents about "offboarding procedures" and "separation agreements" even though the words don't match. This is the retrieval layer behind most enterprise RAG systems and semantic search applications: the component responsible for finding the right content before the language model generates a response from it.

In most deployed AI knowledge systems, retrieval quality determines answer quality more than the model does. A language model can only work with what it's given; if the vector database retrieves the wrong document, the wrong version, or nothing useful, the model will either fabricate an answer or provide an unhelpful one. Retrieval is an information architecture problem: what content is indexed, how current it is, whether access permissions are enforced, and how well the content is structured for search. Organizations that invest in selecting a model but treat the retrieval infrastructure as an afterthought find that their AI assistant gives confident, well-worded answers that are wrong — and that the fix requires rebuilding the data layer, not the model.