A Systematic Review of Hallucination Mitigation in Large Language Models Using Retrieval-Augmented Generation

Large Language Models (LLMs) have become widely used for question answering, education, research, summarization and information support. Despite their usefulness, LLMs may generate false, unsupported or inconsistent information, commonly described as hallucination. Retrieval-Augmented Generation (RAG) addresses this reliability problem by retrieving relevant information from external knowledge sources and supplying it to the language model as contextual evidence before generating a response. This paper studies the role of RAG in reducing factual hallucinations through a review of existing research and a 50 question pilot comparison of responses generated with and without retrieval support. The submitted report records 49 of 50 responses as correct without RAG and all 50 as correct with RAG; it separately presents a clearly labelled illustrative distribution of 46/3/1 versus 48/2/0 for demonstrating response categories. The distinction is retained to avoid presenting illustrative values as observed experimental results. The study also examines limitations including poor retrieval, unreliable sources, incomplete context and incorrect interpretation of retrieved evidence. The paper concludes that RAG is a practical hallucination-reduction approach, but it should be combined with high-quality retrieval, evidence verification and human oversight for critical applications.