Reducing Hallucination in Large Language Models using Retrieval-Augmented Generation

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation; however, the issue of hallucination remains a significant limitation affecting their reliability and real-world applicability. This paper presented a structured approach to mitigate hallucination by integrating RetrievalAugmented Generation (RAG) with additional verification mechanisms. The proposed system enhances traditional RAG architecture by incorporating dense retrieval, crossencoder re-ranking, context augmentation, and verification techniques such as self-consistency and semantic entropy. By grounding the generated responses in externally retrieved knowledge, the system reduces reliance on parametric memory, thereby improving factual accuracy. Furthermore, the inclusion of verification mechanisms ensures that uncertain or inconsistent outputs are identified and handled appropriately. The study also analysed existing literature, including approaches such as RLHF, FActScore, and SelfCheckGPT, and demonstrated how combining retrieval and verification leads to a more robust and trustworthy system. Compared to conventional generation-based models, the proposed framework provides improved consistency, reduced hallucination, and better alignment with factual information. Despite these improvements, challenges such as retrieval latency, dependency on knowledge base quality, and handling ambiguous queries still persist. Future work can focus on integrating realtime data retrieval, multi-hop reasoning, adaptive confidence scoring, and hybrid knowledge systems to further enhance performance. In conclusion, the proposed hybrid approach of combining retrieval and verification represents a promising direction toward building reliable and trustworthy AI systems for critical applications.

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