RAG-Fusion: a New Take on Retrieval-Augmented Generation
This is an incremental improvement for users needing quick product information in business contexts.
The study tackled the problem of rapidly obtaining product information for engineers, account managers, and customers by evaluating RAG-Fusion, a method combining retrieval-augmented generation and reciprocal rank fusion, and found it provided accurate and comprehensive answers through generated queries contextualizing the original query from various perspectives.
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.