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In this paper, we presented our approach to developing effective RAG-based chatbots, highlighting our experiences of building three chatbots at NVIDIA. We outlined our FACTS framework, emphasizing the importance of content freshness (F), architecture (A), LLM cost (C) management, planning for testing (T), and security (S) in creating robust, secure, and enterprise-grade chatbots. We also identified and elaborated on fifteen critical control points within RAG pipelines, providing strategies to enhance chatbot performance at each stage. Furthermore, our empirical analysis reveals the trade-offs between accuracy and latency when comparing large and small LLMs. This paper offers a holistic perspective on the essential factors and practical solutions for building secure and efficient enterprise-grade chatbots, making a unique contribution to the field. More work is needed in several areas to build effective RAG-based chatbots. This includes developing agentic architectures for handling complex, multi-part, and analytical queries; efficiently summarizing large volumes of frequently updated enterprise data; incorporating auto-ML capabilities to optimize various RAG control points automatically; and creating more robust evaluation frameworks for assessing subjective responses and conversations.

在本文中，我们介绍了开发有效的基于RAG的聊天机器人的方法，重点介绍了我们在NVIDIA构建三个聊天机器人的经验。我们概述了我们的FACTS框架，强调了内容新鲜度（F）、架构（A）、LLM成本（C）管理、测试计划（T）和安全性（S）在创建稳健、安全和企业级聊天机器人中的重要性。我们还识别并详细说明了RAG管道中的十五个关键控制点，提供了在每个阶段增强聊天机器人性能的策略。此外，我们的实证分析揭示了在比较大型和小型LLM时准确性和延迟之间的权衡。本文提供了关于构建安全高效的企业级聊天机器人的基本因素和实用解决方案的整体视角，为该领域做出了独特贡献。在多个领域仍需进行更多工作以构建有效的基于RAG的聊天机器人。这包括开发用于处理复杂、多部分和分析查询的代理架构；高效总结大量频繁更新的企业数据；结合自动机器学习功能以自动优化各种RAG控制点；以及创建更稳健的评估框架以评估主观响应和对话。