Dynamic Query Routing with Aleatoric and Epistemic Uncertainty Handling for Virtual Assistants: A Hybrid Approach in Retrieval-Augmented Generation

Virtual assistants need effective query processing in orderto provide precise responses in uncertain, knowledge-rich scenarios.This work introduces a hybrid system for dynamic query routing anduncertainty-sensitive response generation in a retrieval-augmented generation approach. The system combines query embedding, classification,and uncertainty management in routing queries over heterogeneousknowledge sources, with subsequent response generation. The systemcounters aleatoric uncertainty through the analysis of semantics inthe query, with epistemic uncertainty managed through confidencecalibration, supporting robust performance. The evaluations yield91 percent routing accuracy in routing, an Expected Calibration Error(ECE) of 0.06, a 95 percent Uncertainty-Handled Query Success Rate(UH-QSR), and an average response time of 0.55 s, achieving 15 percentimprovement in accuracy as well as 50 percent speedup over thebaseline. The system’s efficiency and resilience are supported throughnovel quantities of a Routing Efficiency Index (REI) of 18.2 and Quality Efficiency Ratio (QER) of 1.60. The presented approach contributes tovirtual assistant systems with scalable solutions for conversational AIapplications.