Llm

Retrieval-Augmented Generation (RAG) has evolved rapidly in recent times. The original RAG paradigm was designed to be straightforward: retrieve the most relevant chunks from a corpus and use them to answer a question. While effective for many document-centric queries, this approach begins to struggle when questions extend beyond the boundaries of a single document. Or…