Vector Search
Vector Search is the technique of finding semantically similar documents by comparing their embeddings. It powers retrieval in AI search engines and RAG systems.
Definition
Vector search converts a user's query into an embedding, then compares it against embeddings of millions of documents to find the most similar ones. This happens in milliseconds using specialized databases and distance metrics like cosine similarity. Unlike keyword search, vector search finds conceptually related content even when vocabulary differs.
AI search engines use vector search as their primary retrieval mechanism, often combined with traditional keyword search for precision. A vector search might find your case study about 'converting users' when someone asks about 'customer retention', even though no words match, because the embeddings represent related concepts.
Why it matters for AI visibility
Vector search enables AI engines to find your brand's content based on topical relevance, not keyword repetition. Brands that own semantic territory in their space, expressing ideas in multiple ways and from multiple angles, become more likely to appear in answers. This makes content strategy more important than keyword gaming.
Related terms
Embeddings
Embeddings are numerical representations of text, converting words, phrases, or documents into lists of numbers that capture their meaning. AI search engines use embeddings to find relevant sources for answers.
SEOSemantic Search
Semantic Search is the search engine capability to understand the meaning, context, and intent of search queries and content, going far beyond simple keyword matching. It interprets synonyms, recognizes relationships between concepts, disambiguates ambiguous terms, and matches user intent to relevant pages. Semantic understanding powered by natural language processing and knowledge graphs is fundamental to modern search ranking.
AIRetrieval-Augmented Generation (RAG)
RAG is the technique of retrieving relevant documents at answer time, then using them to ground an LLM's response. It enables AI search engines to cite sources while generating answers.
AIHybrid Search
Hybrid Search combines vector search with keyword search, leveraging both semantic similarity and exact term matching to retrieve relevant documents. It provides more robust results than either method alone.