Embedding

An embedding turns text — or images, audio, code — into a fixed-length vector of numbers, in such a way that semantically similar inputs end up close together in vector space. That is what enables 'meaning-based' search: a query about 'returning a pair of shoes' can match a document titled 'exchanging footwear' even when no keyword overlaps.

Technically, embeddings are produced by Transformer-class models and usually live in 512 to 4,096 dimensions. OpenAI's text-embedding-3, Cohere's embed v3 and open models like BGE and E5 are typical choices. Input text is tokenised, run through the model and pooled into a single vector, which is then compared against other vectors with cosine similarity or a similar metric. That comparison is the core operation behind every semantic search system.

For marketers, embeddings are the foundation of the entire GEO infrastructure stack: vector databases, semantic search and RAG all sit on top. Because AI looks at content as meaning rather than literal keywords, synonyms, phrasing and context drive visibility directly. Pages stuffed with the keyword 'AI marketing tool' lose to pages that genuinely describe scenarios, outcomes and edge cases — those land in more parts of the embedding space and match more queries. Modern keyword strategy is quietly turning into embedding-friendliness strategy.

A frequent misread is that embeddings are a 'set and forget' artefact. They are not. The same text produces completely different vectors depending on the model. OpenAI, Cohere and BGE live in incompatible embedding spaces, so swapping models means rebuilding the entire index. Another trap: embeddings capture similarity, not truth. 'Close in vector space' is a relevance signal, never a correctness guarantee, and people forget that more often than they should.

In Korea, embedding model choice has a direct impact on GEO outcomes. English-centric models often handle Korean honorifics, compound words and synonyms poorly, so identical content can score lower for Korean queries than for English ones. Using multilingual embeddings, and routinely evaluating retrieval quality on Korean test sets, is increasingly the default operating procedure for Korean brands building serious RAG and GEO pipelines.