Vector embedding · Vector representation
An embedding is the vector representation of a text in a mathematical space, where closeness between two vectors reflects closeness in meaning. It is the basis of semantic search and RAG: the engine compares the question’s vector with those of indexed passages.
Standalone definition · 42 words
How does an embedding work?
A model turns a text into a sequence of numbers, a vector. Two texts with similar meaning give nearby vectors, measured for example with cosine similarity. Unlike keyword search, “cheap electric car” can then retrieve a passage about an “affordable electric vehicle”.
Why do embeddings matter for GEO?
Passage retrieval by AI engines relies largely on this semantic closeness. A clear passage focused on one topic produces a sharper vector. The US antitrust decision cited by Search Engine Journal also mentions embedding-based retrieval in Google’s systems.
How do you use them in practice?
Write focused sections, without mixing several topics in one block. On the analysis side, clustering and cannibalization-detection tools use embeddings to group similar content and spot duplicate intents.
Frequently asked questions
Is an embedding specific to a model?
Yes. Vectors produced by two models are not comparable with each other.
Do embeddings replace keywords?
They complement lexical search. Hybrid systems combine both, often merged with RRF.
Can you see a page’s embedding?
Not the one used by mainstream engines. You can compute one with an embeddings API for your own analyses, with no match to any engine’s.
Related terms
Further reading
- Techniques to optimise for AI Overviews and LLMs
- RRF, Reciprocal ranking fusion tech class the answers on the LLMs (Chatgpt, Gemini)
- ← Back to the GEO glossary (114 terms)