An Analytical Study of Behavior-Aware Retrieval-Augmented Generation Frameworks in Enterprise Software Ecosystems for Optimizing User Navigation and Decision Support

Abstract

In modern enterprise applications, Retrieval-Augmented Generation (RAG) has emerged as a powerful mechanism for knowledge delivery. However, conventional RAG frameworks operate in a behavioral vacuum, relying solely on text-based queries while ignoring the user's operational context, navigation history, and implicit intent. This lack of behavioral awareness frequently results in fragmented user journeys, information overload, and suboptimal decision support. To address these limitations, this study introduces a novel Behavior-Aware Retrieval-Augmented Generation (BA-RAG) framework specifically engineered for enterprise ecosystems. The proposed architecture captures real-time user telemetry—including navigation pathways, query velocity, and interaction history—and transforms these signals into behavioral context vectors. During the retrieval phase, these context vectors are mathematically fused with standard semantic search embeddings to rerank knowledge inputs prior to Large Language Model (LLM) generation. The framework was evaluated via a controlled user study (N=45) within a simulated enterprise Resource Planning (ERP) environment. The experimental results demonstrate that the behavior-aware framework yielded a 25% reduction in user navigation steps, a drop in average task completion time from 8.4 to 6.3 minutes, and enhanced retrieval precision, raising the Normalized Discounted Cumulative Gain (NDCG@5) score from 0.71 to 0.88. These findings demonstrate that incorporating real-time user behavioral context is critical for transforming generative AI utilities into proactive workflow accelerators within complex, data-dense corporate environments.