Category: AI Search & Retrieval
Definition:
Vector Search is a search method that finds information based on the similarity of meaning or concepts, rather than relying only on exact keyword matches.
It typically uses embeddings, which represent information as numerical vectors, to identify content that is semantically relevant to a query.
Why it matters:
Vector Search allows AI systems to find relevant information even when the words in the query and the words in the source content are different.
This is particularly important for AI-powered search and RAG systems, where retrieving the most relevant information can directly influence the answer an AI system generates.
Example:
A user asks, “How do I get my business mentioned by ChatGPT?”
A vector search system may retrieve content about AI visibility, GEO, brand mentions, AI citations, and generative search, even if the exact phrase “get my business mentioned by ChatGPT” does not appear in those documents.
Vector Search vs. Keyword Search:
Keyword Search looks primarily for matching words or phrases.
Vector Search compares the semantic meaning of information to find conceptually relevant results.
Related terms:
Embeddings · RAG · Semantic Search · Retrieval · Vector Database · LLM · AI Search · AI Visibility
In simple terms:
Vector Search is a way for AI systems to find information based on meaning rather than just matching words.
