In the rapidly maturing field of Retrieval-Augmented Generation (RAG), the prevailing architecture—the "naive baseline"—is increasingly showing its age. Most enterprise systems operate on a rigid, one-size-fits-all principle: retrieve the top-K document chunks and pass them simultaneously to the Large Language Model (LLM) for processing. While this "batch" approach is robust, it is also economically inefficient, […]
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