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.

通过评估新兴的 RAG-Fusion 方法，本研究发现 RAG-Fusion 通过生成多个查询、使用互惠评分对其进行重新排名并融合文档和得分，从多个角度对原始查询进行了上下文化，使其能够提供准确和全面的答案。然而，在生成查询与原始查询关联度不足时，部分答案可能偏离主题。该研究在人工智能和自然语言处理应用中取得了重大进展，并展示了全球和多行业背景下的转变。