AI
Vector search
Finding nearest neighbors in embedding space. Core of RAG. Mushy pages cluster together and lose.
Distinct facts move you away from the oatmeal cloud.
You cannot submit a vector to Google. You can write pages that embed distinctly in anyone’s index.
Examples
- Two competitors’ AI-written guides sit on top of each other. A page with a unique failure-mode table sits apart and gets pulled.
Related terms
Embeddings
Vectors that represent meaning. They power semantic search and RAG. Your page becomes a point in space.
Hybrid search
Combining keyword (BM25) and vector retrieval. Many RAG stacks do this. Your nouns and your meaning both count.
RAG (retrieval-augmented generation)
RAG means the model looks up documents at answer time instead of relying only on what it memorized in training.
FAQ
Do keywords still matter? +
Yes for hybrid search and for humans. Vectors don’t make nouns optional.
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