Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/9/zh: Difference between revisions

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At NVIDIA, our main motivation was to improve our employee productivity by building enterprise chatbots. Our initial enthusiasm quickly met with the reality of addressing numerous challenges. We learned that crafting a successful enterprise chatbot, even in the post Chat-GPT era, while promising, is not easy. The process demands meticulous engineering of RAG pipelines, fine-tuning LLMs, and engineering prompts, ensuring relevancy and accuracy of enterprise knowledge, honoring document access control permissions, providing concise responses, including pertinent references, and safeguarding personal information. All of these require careful design, skillful execution, and thorough evaluation, demanding many iterations. Additionally, maintaining user engagement while optimizing for speed and cost-efficiency is essential. Through our journey, we learned that getting an enterprise conversational virtual assistant right is akin to achieving a perfect symphony where every note carries significance!

在NVIDIA，我们的主要动机是通过构建企业聊天机器人来提高员工的生产力。我们的初始热情很快就遇到了许多挑战的现实。我们了解到，即使在Chat-GPT时代之后，打造一个成功的企业聊天机器人虽然充满希望，但并不容易。这个过程需要精心设计RAG管道、微调LLM和工程提示，确保企业知识的相关性和准确性，遵守文档访问控制权限，提供简洁的回答，包括相关的参考资料，并保护个人信息。所有这些都需要精心设计、熟练执行和彻底评估，要求进行多次迭代。此外，在优化速度和成本效益的同时保持用户参与度也是至关重要的。在我们的旅程中，我们了解到，打造一个完美的企业对话虚拟助手就像实现一场完美的交响乐，每一个音符都具有重要意义！