Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.

翻译：摘要：英飞凌公司发现工程师、客户经理及客户需要快速获取产品信息。传统上，这一问题通过检索增强生成（RAG）聊天机器人解决，但在本研究中，我评估了近期流行的RAG-Fusion方法的应用效果。RAG-Fusion结合了RAG与倒数排序融合（RRF）技术：先生成多个查询，通过倒数分数重新排序，再融合文档及对应分数。通过人工评估答案的准确性、相关性和全面性，我发现RAG-Fusion能生成从多角度对原始查询进行语境重构的查询，从而提供准确全面的答案。然而，当生成的查询与原始查询相关性不足时，部分答案会偏离主题。本研究标志着人工智能（AI）与自然语言处理（NLP）应用的重要进展，并展示了其在全球跨行业背景下的变革潜力。