Glossary
Embedding
An embedding is a numeric vector that represents the meaning of a piece of text, so systems can compare passages by similarity rather than exact keyword match. Retrieval layers in AI search embed both the question and candidate content, then select passages whose vectors sit closest to the query.
In plain terms
The engine turns your paragraph into numbers that capture what it means, then finds paragraphs whose numbers are close to the question's.
Why it matters
It explains why synonym-stuffing fails and why clarity wins. A passage that says one thing plainly embeds cleanly; a passage covering five topics embeds as a muddle and matches nothing well.
How to apply it
- Keep each section about one idea.
- Use the vocabulary your buyers use, once, in context — repetition adds nothing.
- Avoid burying the point beneath preamble.
Example
A B2B search team notices that a page titled "reduce employee turnover" gets retrieved for the query "how to keep staff from quitting" even though the two phrases share almost no words, because the retrieval system compared the embeddings of the question and the page and found them numerically close in meaning.
Related terms
Back to the full glossary (75 terms).
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