In a recent YAAP episode, Yuval Belfer and Niv Granot from AI21 Labs discussed the challenges and misconceptions surrounding the evaluation of Retrieval-Augmented Generation (RAG) systems. They emphasized that current RAG benchmarks often fail to reflect real-world complexities, likening the evaluation process to training for a marathon by doing sprints, where the focus is misaligned with practical applications. Granot highlighted two primary issues: the "Chunking Catch-22," where balancing between broad and detailed content divisions is problematic, and the "It's All in One Place" myth, which oversimplifies how information is actually dispersed and interconnected across documents. He used the example from "Seinfeld" to illustrate how RAG systems struggle with nuanced retrieval tasks that involve contextual understanding. The episode suggests that to improve RAG systems, the evaluation process must evolve to better reflect real-world information processing, moving beyond traditional benchmarks to focus on document splitting, integrating scattered information, and understanding inter-document relationships.