Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework

arXiv cs.CL / 4/6/2026

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Key Points

The paper argues that evaluating Retrieval-Augmented Generation (RAG) for enterprise use depends on more than final accuracy, including reasoning complexity, retrieval difficulty, document structure diversity, and explainability requirements.
It claims existing academic RAG benchmarks lack systematic diagnostics for these intertwined failure modes, leading to high benchmark scores that don’t translate into reliable real deployments.
The authors propose a multi-dimensional diagnostic framework with a four-axis difficulty taxonomy to characterize and isolate weaknesses in RAG systems.
They integrate this taxonomy into an enterprise-focused RAG benchmark intended to better identify where systems are likely to fail before operational rollout.
Overall, the work targets improving trust and reliability by enabling more actionable evaluation and deployment readiness checks for RAG.

Abstract

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.