What Are RAG Embeddings?

RAG embeddings convert text into numerical vectors that capture semantic meaning, enabling AI systems to find related concepts even when different words are used. Unlike keyword searches that only match exact terms, embedding models can recognize that "authentication," "login security," and "user verification" represent similar ideas by placing them close together in mathematical space. These vectors are stored in vector databases that use distance metrics like cosine similarity to quickly retrieve relevant documents for RAG systems.