Enhancing Language Models with Retrieval-Augmented Generation for Accurate and Contextual Responses

NONE 20260318074130528 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 1790-0832 2224-3402 Enhancing Language Models with Retrieval-Augmented Generation for Accurate and Contextual Responses Mauro Mazzei Istituto di Analisi dei Sistemi ed Informatica - National Research Council – LabGeoInf, Via dei Taurini, 19, 00185 Rome, ITALY Large language models (LLMs), now also used in production environments, are susceptible to significant inaccuracies and errors, particularly when very specific topics in sectoral domains are involved. Incorrect responses produced by generative systems can cause many problems for non-expert users who, unfamiliar with the specific field of knowledge, are unable to assess the reliability of the responses generated. This problem further amplifies the errors of generative platforms. This paper explores how to mitigate such issues using Retrieval-Augmented Generation (RAG), a technique that enhances LLMs by integrating external information retrieval. RAG helps reduce hallucinations by grounding responses in relevant, retrieved content. The study examines the architecture and implementation of a RAG system and evaluates its effectiveness in improving response accuracy through simple experimental examples. It also investigates techniques and mathematical models to enhance the relevance of retrieved information and discusses the flow of structured and unstructured data into a vector database. 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