You can improve RAG systems by preprocessing your corpus once to add distilled, compact versions of relevant documents—this works with any retrieval method and shows consistent gains without changing your pipeline.
This paper proposes WriteBack-RAG, a method that improves retrieval-augmented generation (RAG) systems by treating the knowledge base as trainable. Using labeled examples, the system identifies relevant documents, distills them into compact knowledge units, and adds these to the corpus.