How do embeddings power semantic search and RAG?

This is where embeddings earn their keep. Semantic search embeds both the query and the documents and ranks by vector similarity, so a search matches meaning rather than keywords. Cohere frames the contrast directly: semantic search “solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles to capture the context or meaning of a piece of text.”^[1] It lets “how do I reset my password” match a page titled “recovering account access.”

The same machinery underpins retrieval-augmented generation. The RAG paper paired a generative model with “a dense vector index of Wikipedia, accessed with a pre-trained neural retriever,” fetching relevant passages and conditioning the answer on them.^[2] The RAG and retrieval page walks through that pipeline end to end.