Embeddings

Vectors that represent meaning. They power semantic search and RAG. Your page becomes a point in space.

Pages that never mention synonyms can still be retrieved if the embedding is good — or missed if the chunk is mush.

Unique numbers and proper nouns make distinctive vectors. Fluff collapses toward the average.

Examples

Two competitors’ “ultimate guides” embed almost on top of each other. A page with a novel dataset sits elsewhere and gets pulled for that query.

Combining keyword (BM25) and vector retrieval. Many RAG stacks do this. Your nouns and your meaning both count.

RAG means the model looks up documents at answer time instead of relying only on what it memorized in training.

Finding nearest neighbors in embedding space. Core of RAG. Mushy pages cluster together and lose.

Can I see my embedding? +

Not in Google. You can embed your own corpus internally to test retrieval. That’s a content QA trick.

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