A vector embedding is a numerical representation of a piece of text (or an image, or an audio clip) in a high-dimensional space, where semantic similarity translates to spatial proximity. It’s the math that lets AI understand meaning, not just keywords.
When an AI answer engine decides whether to cite your content in a response, it’s comparing vector embeddings of the user’s question against embeddings of every page it knows about. Content that’s semantically clear (not keyword-stuffed) wins.