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Retrieval Augmented Generation (RAG) is a process where relevant information is retrieved from vector databases through semantic matching and then fed to LLMs for response generation. In a RAG pipeline, vector databases and LLMs collaboratively ensure the delivery of up-to-date enterprise knowledge. However, RAG pipelines have many control points, each of which when not tuned well can lead to lower accuracy, hallucinations, and irrelevant responses by Chatbots. Additionally, document access control permissions complicate the search and retrieval process, requiring careful management to ensure data security and relevance. Furthermore, multi-modal content necessitates the use of multi-modal retrievers to handle structured, unstructured, and semi-structured data, including presentations, diagrams, videos, and meeting recordings. Addressing these challenges is critical for maintaining the accuracy and reliability of enterprise chatbots. Inspired by ([[#bib.bib3|3]]), we identify fifteen control points of RAG from our case studies visualized in Figure [[#S2.F1|1]]. Each control point is labeled with a number. In the remainder of this section, we present our insights and learnings for addressing RAG control points.

检索增强生成（RAG）是一种通过语义匹配从向量数据库中检索相关信息，然后将其提供给大型语言模型（LLM）以生成响应的过程。在RAG管道中，向量数据库和LLM协作确保提供最新的企业知识。然而，RAG管道有许多控制点，如果调校不当，可能导致聊天机器人准确性下降、幻觉和不相关的响应。此外，文档访问控制权限使搜索和检索过程复杂化，需要仔细管理以确保数据安全性和相关性。此外，多模态内容需要使用多模态检索器来处理结构化、非结构化和半结构化数据，包括演示文稿、图表、视频和会议记录。解决这些挑战对于保持企业聊天机器人的准确性和可靠性至关重要。受（3）的启发，我们从案例研究中识别出RAG的十五个控制点，如图1所示。每个控制点都标有一个编号。在本节的其余部分，我们将介绍我们在解决RAG控制点方面的见解和经验。