Vector Search
Vector Search is an advanced information retrieval method that uses machine learning to transform text, images, or audio into numerical representations called "vectors." This allows AI to search based on semantic meaning and concepts (finding "canine" when searching for "dog") rather than exact keyword matches.
The Future of "Understanding" Search
Traditional keyword search is binary: your page either contains "red dress" or it doesn't. Vector search is conceptual: a user searching for "outfit for gala" can find your "red evening gown" because the AI understands these concepts are semantically similar, even with zero overlapping words. Modern search bars (Amazon, Netflix, Shopify) increasingly use vector search. For businesses, this means optimizing for intent and concepts, not just keywords. Product descriptions should use rich, contextual language that helps AI models understand what the product is for, who it's for, and what problems it solves—this semantic richness creates better vector embeddings.
Keyword Search vs. Vector Search
Real-World Impact
User searches "cozy mystery books" on keyword-only site
No results (site uses "detective fiction" label)
User leaves frustrated, zero sales
Same search on vector-enabled site
AI understands equivalence, shows detective fiction
User finds perfect match, completes purchase