Glossaire · GEO

Embedding

An embedding (or vector representation) is a numerical encoding of a text, word or document expressed as a vector across hundreds of dimensions. This technique converts the meaning of content into mathematical coordinates, so two texts that are semantically close end up close together in vector space, even when they share no common words. Language models and AI search engines rely on embeddings to compare a question against millions of passages and identify those that best match the intent, regardless of exact keyword matches. In GEO, understanding embeddings is essential: a piece of content is only cited by ChatGPT, Perplexity or AI Overviews when its vector sits close to the vector of the query. Optimizing for embeddings means writing clear, self-contained, semantically dense passages that can be accurately positioned within a generative engine's vector space.

Embeddings are the internal language of AI search engines. Where Google historically indexed words, ChatGPT, Perplexity and AI Overviews reason over vectors. Mastering this concept radically changes how you produce content that shows up in generative answers.

How it works

An embedding model reads a text and turns it into a list of numbers — a vector — that encodes its meaning. Each dimension represents a semantic facet learned by the model. The result: texts with similar meaning get similar vectors, measured by their cosine similarity.

When a user asks a question, that question is also converted into an embedding. The engine compares this vector against those of the passages stored in its vector database and surfaces the closest ones. This mechanism powers RAG, the architecture behind most answers cited by AI engines.

A concrete example

The sentences "how to reduce my customer acquisition cost" and "lower the CAC on my campaigns" share almost no words. To a keyword engine, they are far apart. To an embedding engine, their vectors nearly overlap: content answering one also surfaces for the other.

Key takeaway

In GEO, you no longer optimize for exact words but for semantic proximity. A passage that is clear about a single concept is more citable than a catch-all paragraph.

Why it matters

Citability in AI engines depends directly on the quality of your passages' embeddings. Diluted, vague or multi-topic content produces a "blurry" vector that is poorly positioned and therefore rarely retrieved. Conversely, structured, factual and focused passages land precisely in vector space.

That is the heart of our approach to GEO at LUWIZ: structuring your content so that every passage is correctly understood, vectorized and selected by generative engines.

FAQ

Frequently asked questions

A keyword is a string compared literally, whereas an embedding captures the meaning of a text as a vector. Two sentences with no words in common can have very close embeddings if they discuss the same thing.

Write self-contained, factual and semantically rich passages around a single idea. The more clearly a passage expresses a concept, the better positioned its embedding will be and the more likely it is to be retrieved by an AI engine.

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