Embeddings
Numerical vectors that represent the meaning of text, used by AI to find semantically similar content.
Full definition
An embedding is a high-dimensional vector (typically 768 to 3072 numbers) that represents the meaning of a piece of text. AI search systems compute embeddings for both user queries and indexed pages, then return the pages whose embeddings are closest in vector space.
Embeddings power semantic search — the reason AI engines can match 'how do I cancel my plan' to a page titled 'subscription management' even with no shared keywords. Content that clearly expresses one concept per paragraph has cleaner embeddings and ranks better.
Example
OpenAI's text-embedding-3-large model produces 3072-dimensional vectors.
Related terms
Put it into practice
Run a free OptimAIze scan to see how your site handles Embeddings and the rest of the GEO checklist.
Run free scanFrequently asked questions
Is Embeddings the same as SEO?
No. Embeddings is one piece of the broader GEO (Generative Engine Optimization) program that sits on top of classical SEO. The two work together — classical SEO gets you crawled and indexed; Embeddings is part of what gets you cited by AI engines.
Do I need a tool to implement Embeddings?
For most teams, a free scanner like OptimAIze is enough to identify what's missing. Implementation is usually a copy-paste of generated markup or a small code change — no specialist tool required.