Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning complexity, retrieval difficulty, the diverse structure of documents, and stringent requirements for operational explainability. Existing academic benchmarks fail to systematically diagnose these interlocking challenges, resulting in a critical gap where models achieving high performance scores fail to meet the expected reliability in practical deployment. To bridge this discrepancy, this research proposes a multi-dimensional diagnostic framework by defining a four-axis difficulty taxonomy and integrating it into an enterprise RAG benchmark to diagnose potential system weaknesses.
@article{arxiv.2604.02640,
title = {Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework},
author = {Kenichirou Narita and Siqi Peng and Taku Fukui and Moyuru Yamada and Satoshi Munakata and Satoru Takahashi},
journal= {arXiv preprint arXiv:2604.02640},
year = {2026}
}
Comments
8 pages, 3 figures. Accepted at AAAI 2026 Workshop