What is a Vector Embedding?
A vector embedding is a list of numbers that represents the meaning of a piece of text, so AI systems can compare how similar two texts are. Retrieval systems embed both the user's question and candidate pages, then pull the pages whose embeddings sit closest in meaning, which is how relevant sources reach an AI answer.
Why embeddings matter for GEO
In a retrieval-augmented system, your page competes to be retrieved by semantic similarity, not keyword match. If your content clearly expresses the meaning behind a buyer's question, its embedding lands near that question and the page is more likely to be pulled into the answer.
The practical takeaway
Write content that directly and clearly expresses the ideas your buyers ask about. Semantic clarity, not keyword stuffing, is what makes a page retrievable in an embedding-based system.