  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.
