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Current evaluation methods for Retrieval-Augmented Generation (RAG) systems are flawed because they do not match real-world scenarios. The primary issues are the "chunking catch-22," where the optimal way to split documents is unclear, and the false assumption that answers exist in a single, tidy location. In reality, information is often scattered across multiple documents, requiring contextual understanding that benchmarks don't test for. To improve RAG systems, evaluation must shift to assess how well they handle document splitting, piece together scattered information, and understand relationships between documents.