The paper presents a methodology for uncovering knowledge gaps on the internet using the Retrieval Augmented Generation (RAG) model. By simulating user search behaviour, the RAG system identifies and addresses gaps in information retrieval systems. The study demonstrates the effectiveness of the RAG system in generating relevant suggestions with a consistent accuracy of 93%. The methodology can be applied in various fields such as scientific discovery, educational enhancement, research development, market analysis, search engine optimisation, and content development. The results highlight the value of identifying and understanding knowledge gaps to guide future endeavours.

翻译：本文提出了一种利用检索增强生成（RAG）模型揭示互联网知识缺口的方法。通过模拟用户搜索行为，RAG系统能够识别并弥补信息检索系统中的缺口。研究表明，RAG系统在生成相关建议方面具有持续93%的准确率。该方法可应用于科学发现、教育提升、研究发展、市场分析、搜索引擎优化以及内容开发等多个领域。研究结果凸显了识别并理解知识缺口对指导未来工作的重要价值。