Embeddings
Numerical representations that place similar meanings close together.
Technical Term
Full explanation
Embeddings are numerical representations of words, phrases, or concepts that place similar meanings close together in mathematical space, allowing AI systems to compare meaning rather than exact wording.
Practically, an embedding turns a word, sentence, or whole page into a long list of numbers, a point in a very high-dimensional space. Two pieces of text that mean similar things end up as nearby points, even if they share no words in common; two pieces of text that share many words but mean different things end up far apart.
This is why a page built around one exact keyword, repeated, is not what earns inclusion in an AI answer. What matters is whether the underlying concept is expressed clearly enough that its embedding lands close to the embedding of the question being asked, which rewards plain, well-defined language over keyword density.
Why this matters
They are the mechanism behind meaning-based matching, which is why clearly defined language beats repeated keywords.
Example
The phrases "AI visibility measurement" and "tracking how often a brand shows up in AI answers" share almost no words, but their embeddings sit close together because they mean close to the same thing. A system comparing embeddings would treat a page using either phrasing as relevant to the same question.
Where this sits
Embeddings belongs to AI & Search Infrastructure, one of the seven layers of the Aingworth Glossary. The technical vocabulary behind the interfaces. Useful for understanding how the systems work, without becoming the center of the conversation.
Ready to see where your brand stands?
Start with the free diagnostic to see which condition of the Recognition Triangle is leaking, or read the frameworks these terms belong to.