Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/21/zh: Difference between revisions

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Ensuring the freshness of enterprise data in LLM-powered chatbots presents several challenges. Foundation models, although powerful, often fall short as they lack domain-specific and enterprise-specific knowledge. Once trained, these models are essentially frozen in time and may hallucinate, providing undesired or inaccurate information when used on enterprise content that they are not trained on.

确保企业数据在基于LLM的聊天机器人中保持新鲜性面临诸多挑战。尽管基础模型功能强大，但由于缺乏特定领域和企业特定的知识，它们往往显得不足。一旦训练完成，这些模型基本上就被冻结在某个时间点，可能会出现幻觉，在使用未经过训练的企业内容时提供不需要或不准确的信息。