Embeddings
Embeddings are a representation of text as a vector of numbers, where texts with similar meanings lie side by side. The basis of semantic search and RAG.
Embedding is the translation of text into a vector of numbers so that texts that are similar in meaning appear nearby in space. “Buy sneakers” and “order sneakers” will be close, although the words are different.
Why does a marketer need this? Embeddings are a semantic search engine: you search by meaning, not by exact match of words. They are used to build RAG, clustering of the semantic core, grouping reviews and requests by topic without manual marking.
You usually don’t need to count embeddings yourself - it works “under the hood” of RAG systems and vector databases. But it’s useful to understand the mechanics: that’s why AI search finds something relevant even when you didn’t guess the keyword.
Related terms
Where is it understood in practice?
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