Kapa.ai explains how to prune RAG context to only what the answer needs

Engineers at Kapa.ai describe their technique for pruning RAG (Retrieval-Augmented Generation) context down to only the passages actually needed to answer a given query. Reducing unnecessary context improves model answer quality and lowers inference costs. The approach targets one of the key production challenges in RAG systems: noisy input data.