标题： 为现实应用设计、开发和评估工程化RAG系统 显示英文标题

标题： Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation

摘要： 检索增强生成（RAG）系统正成为将大型语言模型（LLMs）与外部知识结合的关键方法，解决了事实准确性与上下文相关性方面的局限性。 然而，缺乏关于基于RAG实现的实证研究，这些研究在真实世界用例中进行，通过普通用户参与进行评估，并伴有系统性的经验总结记录。 本文介绍了五个针对治理、网络安全、农业、工业研究和医学诊断等实际场景开发的领域特定RAG应用。 每个系统都集成了多语言OCR、通过向量嵌入进行语义检索以及领域适应的LLMs，通过本地服务器或云API部署以满足不同的用户需求。 一项基于网络的评估涉及总共100名参与者，从六个维度评估了这些系统：（i）易用性，（ii）相关性，（iii）透明度，（iv）响应性，（v）准确性，（vi）推荐的可能性。 根据用户反馈和我们的开发经验，我们记录了十二个关键经验教训，突出了影响RAG系统在实践中可靠性和可用性的技术、运营和伦理挑战。 显示英文摘要

摘要： Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics. Each system incorporates multilingual OCR, semantic retrieval via vector embeddings, and domain-adapted LLMs, deployed through local servers or cloud APIs to meet distinct user needs. A web-based evaluation involving a total of 100 participants assessed the systems across six dimensions: (i) Ease of Use, (ii) Relevance, (iii) Transparency, (iv) Responsiveness, (v) Accuracy, and (vi) Likelihood of Recommendation. Based on user feedback and our development experience, we documented twelve key lessons learned, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.