为中小企业提供快速部署RAG工具的敏捷方法
An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
- 基于工程化方法设计角色与流程，降低技术门槛
- 一个月内完成部署，用户反馈驱动迭代优化
- 适合无NLP经验的工业团队，提升数据可靠性
检索增强生成（RAG）已成为缓解大语言模型幻觉和知识过时问题的有效方案。然而，由于资源有限且缺乏自然语言处理专业知识，中小型企业（SMEs）在部署RAG工具方面仍面临挑战。本文提出EASI-RAG——面向工业场景的RAG企业应用支持方法，是一种基于方法工程原则的结构化敏捷流程，包含明确的角色、活动与技术。该方法在一家环境检测实验室的真实案例中得到验证：通过从操作规程中提取数据，构建RAG系统以回答操作员问题。一个无RAG经验的团队在不到一个月内完成部署，并根据用户反馈持续迭代改进。结果表明，EASI-RAG支持快速实施、高用户采纳率、准确答案输出，并提升了底层数据可靠性。本研究展示了在工业SME中部署RAG的可行性。未来工作包括跨多样化场景的泛化能力以及与微调模型的进一步融合。
原文摘要 · Abstract (English)
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emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge.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.
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