Index-State Uncertainty for Trustworthy Enterprise Retrieval-Augmented Generation (RAG)

Abstract

Enterprise Retrieval-Augmented Generation (RAG) systems are increasingly deployed across high-stakes industries in the United States, yet a critical vulnerability persists in the gap between what a RAG system believes its knowledge index contains and what the index actually reflects at query time. This study introduces and operationalizes the concept of Index-State Uncertainty (ISU), a multi-dimensional construct capturing staleness, coverage gaps, temporal conflicts, update latency, and query-index mismatches in enterprise RAG pipelines. Drawing on a cross-sectional dataset of 1,248 enterprise RAG deployments spanning eight sectors including financial services, healthcare, legal and compliance, technology, government, retail, manufacturing, and education, this research develops and validates an ISU-Index score as a predictive measure of RAG trustworthiness. Multivariate regression, canonical correlation analysis (CCA), and sector-specific heatmap profiling are employed to quantify relationships between ISU dimensions and downstream retrieval accuracy and hallucination rates. Results confirm that index staleness duration, coverage gap, and temporal conflict are the most statistically significant determinants of degraded RAG performance, collectively explaining 84.7% of variance in the ISU-Index (Adj. R² = 0.841, F = 142.6, p < 0.001). High-churn sectors such as financial services and retail exhibit ISU-Index scores exceeding 0.70, signaling unacceptable retrieval risk, while low-churn domains such as education and manufacturing maintain scores below 0.40. These findings establish a theoretically grounded, empirically validated framework for diagnosing and mitigating index-state uncertainty in enterprise RAG ecosystems.