Vector embeddings in SEO refers to the use of dense numerical representations of text, images, or other content—generated by neural language models—as part of search ranking and retrieval systems. A vector embedding maps a piece of content to a point in a high-dimensional space such that semantically similar content clusters together, allowing a search engine to retrieve documents that are conceptually related to a query even when they share no keywords in common. Google has incorporated embedding-based retrieval into its search stack through systems such as Neural Matching and MUM, and AI search products such as Perplexity and ChatGPT Search use embedding similarity as a core retrieval signal. For SEO practitioners, the practical implication is that thorough topical coverage, clear conceptual focus, and natural language that aligns with how users describe a topic are more important than exact keyword repetition, because embeddings capture meaning rather than surface form.