Glossary
Vector Embedding
A numerical representation of text in high-dimensional space, where similar meanings sit close together — the underlying data structure behind semantic search and most AI retrieval.
A vector embedding is a list of numbers (typically 384, 768, 1024, or 1536 dimensions) that represents a piece of text in such a way that semantically similar texts have similar vectors. Distance in this space approximates difference in meaning.
Where do vector embeddings come into AI search?
Both at indexing time (the AI engine embeds candidate documents and stores the vectors) and at query time (the user's query is embedded and matched against the document vectors via cosine similarity or similar metrics).
Can you see what your page's embedding looks like?
Not directly from the AI engine's side, but you can run your content through public embedding APIs (OpenAI text-embedding-3, Cohere, Voyage) to inspect how chunks of your page relate to query embeddings — a useful diagnostic for retrieval optimization.
What page structure helps embeddings?
Clear semantic chunks (one idea per paragraph), explicit entity naming, and headings that match query phrasing. Chunks that drift across multiple topics get muddier embeddings and worse retrieval scores.
Part of the Cite Hustle GEO glossary — definitions for generative engine optimization and AI search.