The rapid evolution of natural language processing technologies has significantly enhanced the capabilities of generative models, yet challenges remain in maintaining the accuracy and relevance of information over time. The novel concept of integrating a hierarchical Retrieval Augmented Generation (RAG) system with the Llama model addresses these challenges through enabling real-time access to vast external knowledge bases, thereby significantly improving contextual accuracy and relevance. The hierarchical RAG system, designed with multilevel caching and dynamic query routing mechanisms, optimizes information retrieval efficiency, reduces latency, and enhances the overall performance of the model. Comprehensive evaluations demonstrated that the RAG-enhanced Llama outperformed the baseline model across various metrics, including precision, recall, and F1-score, highlighting its superior capability in real-time information retrieval tasks. The system's adaptability to varying data loads and its efficient management of large-scale databases further underscored its robustness and scalability. Comparisons with traditional retrieval methods revealed the distinct advantages of the hierarchical RAG system, particularly in terms of retrieval speed and accuracy. These findings underscore the transformative potential of the proposed approach in advancing the field of natural language processing, providing a solid foundation for future research and development.