The recent advancements in the field of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) have been notably focused on enhancing explainability, optimizing retrieval processes, and improving the accuracy of generated responses. A significant trend is the integration of hierarchical category paths and guided questioning frameworks to provide more transparent and contextually relevant outputs. These approaches not only improve the user experience by offering detailed explanations but also enhance the overall performance of retrieval tasks. Additionally, there is a growing emphasis on refining the knowledge boundaries within LLMs to better manage dynamic and static knowledge, thereby reducing computational costs and improving efficiency. The field is also witnessing innovations in prompt optimization and query refinement techniques, which aim to bridge information gaps and ensure the retrieval of pertinent data. Notably, these developments are being tested across various domains, including tourism, where the need for precise and contextually appropriate information is paramount. Overall, the research is moving towards more sophisticated, user-centric, and computationally efficient solutions that leverage the strengths of both retrieval models and LLMs.