优化与评估企业检索增强生成（RAG）：内容设计视角
Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
AI总结：
本文从内容设计视角分享企业级RAG的构建经验，提出模块化、与模型无关的解决方案策略，发现简单调整知识库内容创建方式对RAG成功影响巨大，并采用灵活的“以人为先”方法监控评估新用户问题的响应。
摘要：
检索增强生成（RAG）是使用大语言模型（LLM）构建客户支持和问答解决方案的流行技术。在本文中，我们分享了团队在构建和维护企业级RAG解决方案方面的实践经验，这些解决方案基于产品文档回答用户关于我们软件的问题。我们的经验并不总是与RAG文献中最常见的模式相匹配。本文重点关注模块化且与模型无关的解决方案策略。例如，过去几年中，在使用不同的搜索方法、LLM以及许多知识库集合的过程中，我们的经验是，对创建知识库内容的方式进行简单更改可以对RAG解决方案的成功产生巨大影响。在本文中，我们还讨论了如何监控和评估结果。常见的RAG基准评估技术对于评估对新用户问题的响应并不有效，因此我们发现需要一种灵活的“以人为先”的方法。
英文摘要：
Retrieval-augmented generation (RAG) is a popular technique for using large language models (LLMs) to build customer-support, question-answering solutions. In this paper, we share our team's practical experience building and maintaining enterprise-scale RAG solutions that answer users' questions about our software based on product documentation. Our experience has not always matched the most common patterns in the RAG literature. This paper focuses on solution strategies that are modular and model-agnostic. For example, our experience over the past few years - using different search methods and LLMs, and many knowledge base collections - has been that simple changes to the way we create knowledge base content can have a huge impact on our RAG solutions' success. In this paper, we also discuss how we monitor and evaluate results. Common RAG benchmark evaluation techniques have not been useful for evaluating responses to novel user questions, so we have found a flexible, "human in the lead" approach is required.
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。
