RAG 并不适用于企业
RAG Does Not Work for Enterprises
AI总结：
本文针对企业场景中 RAG 面临的安全、准确性、可扩展性和集成挑战，调研现有方法并提出包含定量测试、定性分析、消融研究和行业案例研究的评估框架，以验证专用 RAG 架构在企业级部署中的有效性。
摘要：
检索增强生成（RAG）通过引入知识检索来提高大语言模型输出的准确性和相关性。然而，在企业中实施 RAG 面临数据安全、准确性、可扩展性和集成方面的挑战。本文探讨了企业级 RAG 的独特需求，调研了当前的方法及其局限性，并讨论了语义搜索、混合查询和优化检索方面的潜在进展。本文提出了一个评估框架来验证企业级 RAG 解决方案，包括定量测试、定性分析、消融研究和行业案例研究。该框架旨在帮助证明专用构建的 RAG 架构能够在具备企业级安全性、合规性和集成能力的同时，提供准确性和相关性的改进。文章最后总结了企业部署的影响、局限性以及未来的研究方向。研究人员与行业合作伙伴之间的紧密协作可能会加速检索增强生成技术的开发和部署进程。
英文摘要：
Retrieval-Augmented Generation (RAG) improves the accuracy and relevance of large language model outputs by incorporating knowledge retrieval. However, implementing RAG in enterprises poses challenges around data security, accuracy, scalability, and integration. This paper explores the unique requirements for enterprise RAG, surveys current approaches and limitations, and discusses potential advances in semantic search, hybrid queries, and optimized retrieval. It proposes an evaluation framework to validate enterprise RAG solutions, including quantitative testing, qualitative analysis, ablation studies, and industry case studies. This framework aims to help demonstrate the ability of purpose-built RAG architectures to deliver accuracy and relevance improvements with enterprise-grade security, compliance and integration. The paper concludes with implications for enterprise deployments, limitations, and future research directions. Close collaboration between researchers and industry partners may accelerate progress in developing and deploying retrieval-augmented generation technology.
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。
