Why it matters to a buyer
Because embedding-based retrieval matches by meaning rather than exact wording, a page can be retrieved and cited for a question phrased quite differently from its own text, which is a real change from classic keyword matching, but it also means content that is vague or generic in meaning, even if keyword-rich, can retrieve poorly. For a buyer, this is a reason specific, concrete, well-scoped content tends to outperform broad, padded content in AI retrieval, separate from how it performs in ordinary keyword-based search.
How it is measured or applied
Content is converted into embeddings by an embedding model, then stored in a vector database that can quickly find the stored chunks whose embeddings sit closest, in meaning, to a query's own embedding at the moment someone asks a question. Applying this on the content side means writing clear, self-contained, topically focused sections, since ambiguous or overly broad passages produce embeddings that match too many unrelated queries or too few relevant ones. There is no external tool that shows a specific page's embedding, so its practical effect is judged indirectly, through whether the page gets retrieved and cited for the range of questions it was meant to answer.
Sources
- Embeddings, OpenAI
