Embeddings (vector representations)
Embeddings represent a text as a numerical vector that captures its meaning: two semantically close passages have close vectors, even without a shared word. Engines use them to match queries with content.
In practice
For an editor, two practical uses: measuring similarity between your own pages — our internal threshold blocks two neighboring pages that are too alike — and verifying that a piece of content actually covers the semantic field of its target. Keyword stuffing is finished. Meaning is now measurable, literally.