This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. With recent advancements in generative AI technology, LLMs have gained prominence across various domains. In this context, the research addresses the challenge of information scarcity and proposes specific remedies by harnessing LLM capabilities. The investigation delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. The study delves into the efficacy of employing fine-tuning techniques and direct document integration to alleviate data insufficiency. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model, which tackles the aforementioned challenges. The RAG model is carefully designed to enhance information storage and retrieval processes, ensuring improved content generation. The research elucidates the key phases of the information storage and retrieval methodology underpinned by the RAG model. A comprehensive analysis of these steps is undertaken, emphasizing their significance in addressing the scarcity of data. The study highlights the efficacy of the proposed method, showcasing its applicability through illustrative instances. By implementing the RAG model for information storage and retrieval, the research not only contributes to a deeper comprehension of generative AI technology but also facilitates its practical usability within enterprises utilizing LLMs. This work holds substantial value in advancing the field of generative AI, offering insights into enhancing data-driven content generation and fostering active utilization of LLM-based services within corporate settings.

翻译：本研究提出了一种利用大型语言模型（LLM）应用架构实现生成式AI服务的方法。随着近年来生成式AI技术的进步，LLM已在各个领域崭露头角。在此背景下，本研究针对信息稀缺的挑战，通过利用LLM的能力提出了具体的应对策略。研究深入探讨了缓解数据不足问题的方案，并提供了定制化的解决方案。本研究评估了采用微调技术与直接文档集成来缓解数据不足的有效性。本工作的一项重要贡献是开发了一种检索增强生成（RAG）模型，以应对上述挑战。该RAG模型经过精心设计，增强了信息存储与检索过程，确保了内容生成的优化。研究阐明了基于RAG模型的信息存储与检索方法论的关键阶段，并对这些步骤进行了全面分析，强调了它们在解决数据稀缺问题中的重要性。本研究通过实例展示了所提出方法的有效性及其适用性。通过实施RAG模型进行信息存储与检索，该研究不仅加深了对生成式AI技术的理解，还促进了企业利用LLM实现实际应用的可能性。本工作在推动生成式AI领域发展方面具有重要价值，为增强数据驱动的内容生成及在企业环境中积极利用基于LLM的服务提供了洞见。