Retrieval-Augmented Generation (RAG) models combine a retrieval component—often a vector‐based search over an external knowledge source—with a generative language model. The basic RAG pipeline is: Query encoding & retrieval Cross-correlation among retrieved contexts Generator conditioning Why cross-correlation matters in RAG Reducing redundancy: When multiple retrieved chunks say the same thing, naive concatenation wastes tokens. Cross-correlation …