# RAG-Fusion: a New Take on Retrieval-Augmented Generation Source: [RAG-Fusion: a New Take on Retrieval-Augmented Generation]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:26:06.010Z Authors: Zackary Rackauckas Published: 2024-01-31T22:06:07.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Video Retrieval on V-RAGBench](): Video Retrieval; V-RAGBench; Recall@5: 46.3. - [Video Generation on V-RAGBench](): Video Generation; V-RAGBench; Pass Rate (Qwen3-VL-8B): 18. ## Original sources and implementation [Original paper]() [PDF]()