Impact of the Intelligent Assistants with RAG on Information Access and Consultations in Local Governments: A Systematic Review

Abstract

The need to ensure clear, timely, and equitable citizen access to public information has driven local governments to modernize their service processes. Within this framework, Intelligent Assistants with Retrieval-Augmented Generation (RAG) are emerging as a promising solution, although scientific evidence regarding their impact remains scattered and requires critical systematization. This paper aims to determine the impact of RAG-based intelligent assistants on query resolution and information access in local governments. A systematic literature review was conducted following the PRISMA methodology, analyzing 80 open-access papers published between 2020 and 2025 in IEEE Xplore, Scopus, ScienceDirect, ACM Digital Library, Wiley Online Library, and Taylor & Francis Online. The findings indicate that the most frequently used algorithms in e-commerce are Random Forest, SVM, and neural networks; that Python predominates as the development language, followed by Scala and Matlab; that most studies are published in Q1 journals with high academic rigor; and that the most recurrent keywords emphasize classification, prediction, and user experience. This paper provides a solid foundation for future research, guiding the development of more diverse and methodological approaches in the use of intelligent assistants with RAG in local governments.