What is vector search?

Vector search is a method of searching data using mathematical representations known as vectors or embeddings. Instead of matching exact words, vector search evaluates how similar two pieces of content are in meaning. This allows users to find relevant information even when different terminology is used.

In modern AI systems, text, images, audio, and other content can be transformed into high-dimensional vectors using machine learning models. These vectors capture semantic meaning, enabling systems to compare content based on context rather than literal keyword overlap. Similar items are located using distance metrics such as cosine similarity or Euclidean distance.

For Business Analysts, understanding vector search is increasingly important because many AI-enabled applications rely on it. Examples include enterprise knowledge bases, customer support assistants, recommendation engines, document discovery platforms, and retrieval-augmented generation (RAG) solutions used with large language models.

When discussing vector search in an interview, focus on the business value rather than mathematical details. Explain that it improves search relevance, helps users discover information more efficiently, and enables AI systems to retrieve contextually relevant content even when exact keywords are not present.