Enhancing Accuracy of LLM in Nursing Education Through RAG and Thought Chains

The penetration of large language models (LLMs) into all walks of life requires finding effective ways to improve the accuracy of their practical application in nursing scenarios. This study investigates the potential of retrieval-augmented generation (RAG) technology and the chain of thought (CoT) reasoning process in addressing the limitations of LLMs in professional knowledge and complex problem reasoning. Leveraging a knowledge base derived from the Chinese National Nursing Licensure Examination question bank, researchers first evaluated the baseline performance of LLMs. Subsequently, the CoT reasoning process was systematically compared with official exam parsing methods to assess the model's ability to interpret nursing-related questions. Experimental results demonstrated that integrating the knowledge base significantly improved LLM accuracy from 84.58% to 93.33%. Furthermore, the CoT reasoning process achieved a 91.33% accuracy rate in parsing question options, highlighting its robust logical reasoning capabilities. These findings underscore that the synergistic integration of RAG and CoT enhances the precision of LLMs in knowledge retrieval and clinical reasoning, offering an innovative technical pathway for developing intelligent nursing education tools. The study not only validates the effectiveness of combining knowledge augmentation with advanced reasoning mechanisms but also provides methodological insights for improving the reliability of AI applications in health care.