一种在工业中小企业中实施检索增强生成工具的敏捷方法
An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
AI总结：
本文提出EASI-RAG敏捷方法，通过明确角色、活动和技术，帮助资源有限的工业中小企业快速部署检索增强生成工具，并在环境测试实验室案例中验证了其快速实施、高采用率和准确回答的效果。
摘要：
检索增强生成（RAG）已成为缓解大型语言模型（LLM）局限性（如幻觉和知识过时）的有效解决方案。然而，在中小企业（SME）中部署基于RAG的工具仍然面临挑战，原因在于其资源有限且缺乏自然语言处理（NLP）方面的专业知识。本文介绍了EASI-RAG，即面向工业RAG的企业应用支持，这是一种结构化的敏捷方法，旨在促进RAG系统在工业中小企业环境中的部署。EASI-RAG基于方法工程原理，包含明确定义的角色、活动和技术。该方法通过一个环境测试实验室的真实案例研究进行了验证，在该案例中实施了一个RAG工具，利用从操作规程中提取的数据来回答操作员的查询。该系统由一个没有RAG经验的团队在不到一个月的时间内完成部署，随后根据用户反馈进行了迭代改进。结果表明，EASI-RAG支持快速实施、高用户采用率，能够提供准确的答案，并增强了底层数据的可靠性。这项工作凸显了RAG在工业中小企业中部署的潜力。未来的工作包括在不同用例中推广的需求，以及与微调模型的进一步集成。
英文摘要：
Retrieval-Augmented Generation (RAG) has emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge. However, deploying RAG-based tools in Small and Medium Enterprises (SMEs) remains a challenge due to their limited resources and lack of expertise in natural language processing (NLP). This paper introduces EASI-RAG, Enterprise Application Support for Industrial RAG, a structured, agile method designed to facilitate the deployment of RAG systems in industrial SME contexts. EASI-RAG is based on method engineering principles and comprises well-defined roles, activities, and techniques. The method was validated through a real-world case study in an environmental testing laboratory, where a RAG tool was implemented to answer operators queries using data extracted from operational procedures. The system was deployed in under a month by a team with no prior RAG experience and was later iteratively improved based on user feedback. Results demonstrate that EASI-RAG supports fast implementation, high user adoption, delivers accurate answers, and enhances the reliability of underlying data. This work highlights the potential of RAG deployment in industrial SMEs. Future works include the need for generalization across diverse use cases and further integration with fine-tuned models.
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。
