KNWN Visibility
What is Embedding Relevance?
A KNWN glossary definition of Embedding Relevance in the context of AI visibility, search and brand discovery.
Definition
Embedding relevance is the similarity or task fit between vector representations used by a particular retrieval system. It depends on the embedding model, query, indexed unit and similarity method; it is not a public website authority score.
Why it matters
In a controlled retrieval application, embedding relevance affects which passages become candidates. Evaluate it with labeled queries and expected documents. Google explains embeddings as learned vector representations in its machine-learning guide.
Example
A support index splits documentation into passages. For 100 labeled questions, the team measures whether the correct passage appears in the top five and reviews misses. Changing the chunking or embedding model requires rerunning the same evaluation.
Common misconceptions
- Vector closeness proves truth.
- One embedding model represents all meaning.
- Public search engines expose their vector scores.
- Keyword repetition reliably improves embedding retrieval.
Interpretation note
Definitions describe terminology; they do not establish an official ranking factor or guarantee an outcome. Confirm platform-specific terminology against primary documentation.