Redefining Retrieval in RAG A nice comprehensive study that focuses on the components needed to improve the retrieval component of a RAG system. Confirms that the position of relevant information should be placed near the query. The model will struggle to attend to the information if this is not the case. Surprisingly, it finds that related documents don't necessarily lead to improved performance for the RAG system. Even more unexpectedly, irrelevant and noisy documents can actually help drive up accuracy if placed correctly. We need more systematic studies around RAG. The hard part of a RAG system is typically the retriever component. Just dumping relevant docs into the context is not an effective approach but it's what a lot of LLM devs do. I like that the Ragas library proposes the use of several metrics for assessing a RAG system at both the generation and retrieval stages, including an end-to-end evaluation. It's a good first step but we still need better ways to integrate external information that can be effectively leveraged by the generative component.