基于企业数据的 LLM 应用架构的生成式 AI 服务实现研究
A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture
AI总结：
本研究提出基于企业数据的LLM应用架构实现生成式AI服务，通过开发RAG模型并结合微调与文档集成技术，有效缓解数据不足问题，提升信息存储检索与内容生成能力，促进LLM在企业中的实际应用。
摘要：
本研究提出了一种利用大型语言模型（LLM）应用架构来实现生成式 AI 服务的方法。随着生成式 AI 技术的最新进展，LLM 在各个领域日益凸显。在此背景下，本研究针对信息稀缺的挑战，提出通过发挥 LLM 的能力来提供具体的补救措施。研究深入探讨了缓解数据不足问题的策略，并提供了定制化的解决方案。本文考察了采用微调技术和直接文档集成来缓解数据不足的有效性。本研究的一项重要贡献是开发了检索增强生成（RAG）模型，以应对上述挑战。RAG 模型经过精心设计，旨在增强信息存储和检索过程，确保改进内容生成。研究阐述了以 RAG 模型为支撑的信息存储与检索方法的关键阶段。本文对这些步骤进行了全面分析，强调了其在解决数据稀缺问题中的重要性。研究突出了所提方法的有效性，并通过实例展示了其适用性。通过实现 RAG 模型进行信息存储与检索，本研究不仅有助于更深入地理解生成式 AI 技术，还促进了其在利用 LLM 的企业中的实际可用性。这项工作对推进生成式 AI 领域具有重要价值，为增强数据驱动的内容生成以及促进企业环境中基于 LLM 服务的积极利用提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
