用RAG聊天机器人查企业信息，平均省下80%~95%搜索时间。
A survey: Information search time optimization based on RAG (Retrieval Augmentation Generation) chatbot
- 用RAG聊天机器人替代传统搜索，实现快速精准检索。
- 实测显示信息查找时间减少80%至95%。
- 适合需要高效获取内部知识的团队与企业使用。
基于检索增强生成（RAG）的聊天机器人不仅可用于问答式信息检索，还能基于注入的私有数据支持复杂决策。本文调研了在一家名为“X Systems”（保密公司）的企业中，使用RAG聊天机器人相较于传统搜索方法，在检索复杂信息时可节省多少搜索时间。针对相同查询，对比了标准搜索技术与RAG聊天机器人的信息检索耗时。结果显示，使用RAG聊天机器人不仅能显著缩短信息检索时间，还能有效优化搜索流程。本研究对105名跨部门员工进行了测试，以每条查询的平均信息检索时间为评估指标。结果表明，使用RAG聊天机器人相比传统搜索，平均节省80%~95%的搜索时间。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) based chatbots are not only useful for information retrieval through questionanswering but also for making complex decisions based on injected private data.we present a survey on how much search time can be saved when retrieving complex information within an organization called "X Systems"(a stealth mode company) by using a RAG-based chatbot compared to traditional search methods. We compare the information retrieval time using standard search techniques versus the RAG-based chatbot for the same queries. Our results conclude that RAG-based chatbots not only save time in information retrieval but also optimize the search process effectively. This survey was conducted with a sample of 105 employees across departments, average time spending on information retrieval per query was taken as metric. Comparison shows us, there are average 80-95% improvement on search when use RAG based chatbot than using standard search.
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