Retrieval Augmented Generation for automating enterprise technical queries

Abstract

The growing complexity of enterprise IT systems, coupled with an increasing volume of technical support queries, continues to burden support teams, especially during high-demand periods. A significant portion of these queries are repetitive, leading to agent fatigue, delayed resolutions, and reduced customer satisfaction. This study presents a Retrieval-Augmented Generation (RAG) system for automating the resolution of enterprise technical queries. The system integrates a retrieval module that identifies relevant content from a domain-specific knowledge base and a generative module that produces contextually appropriate responses using pre-trained language models. Its performance was evaluated using Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L) metrics, alongside expert qualitative assessments. Results demonstrate that the RAG-based approach improves response quality and fluency, reduces manual workload, and accelerates resolution times. This study demonstrates the practical value of using RAG systems to automate repetitive support tasks in enterprise environments.

Keywords: Natural Language Processing (NLP), Information Retrieval (IR), Retrieval-Augmented Generation (RAG), IT Support Systems, Generative AI.