# RAG-Fusion: a New Take on Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2402.03367 · HTML: https://mlsanity.com/paper/2402.03367/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Re2G: Retrieve, Rerank, Generate](https://mlsanity.com/paper/2207.06300/) (2022)
- [An Analysis of Fusion Functions for Hybrid Retrieval](https://mlsanity.com/paper/2210.11934/) (2022)
- [Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference](https://mlsanity.com/paper/2310.03184/) (2023)

## Built on by (most cited first, 30 of 42)

- [LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation](https://mlsanity.com/paper/2402.11943/) (2024)
- [DMQR-RAG: Diverse Multi-Query Rewriting for RAG](https://mlsanity.com/paper/2411.13154/) (2024)
- [Multi-Head RAG: Solving Multi-Aspect Problems with LLMs](https://mlsanity.com/paper/2406.05085/) (2024)
- [Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation](https://mlsanity.com/paper/2502.03233/) (2025)
- [Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective](https://mlsanity.com/paper/2410.12812/) (2024)
- [Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration](https://mlsanity.com/paper/2505.20625/) (2025)
- [HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA](https://mlsanity.com/paper/2402.01767/) (2024)
- [FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline](https://mlsanity.com/paper/2410.13959/) (2024)
- [CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writing](https://mlsanity.com/paper/2503.12436/) (2025)
- [Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis](https://mlsanity.com/paper/2504.05324/) (2025)
- [syftr: Pareto-Optimal Generative AI](https://mlsanity.com/paper/2505.20266/) (2025)
- [RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets](https://mlsanity.com/paper/2511.01386/) (2025)
- [Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior](https://mlsanity.com/paper/2603.29979/) (2026)
- [REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing](https://mlsanity.com/paper/2506.16444/) (2025)
- [Taxonomy of the Retrieval System Framework: Pitfalls and Paradigms](https://mlsanity.com/paper/2601.20131/) (2026)
- [URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2603.19281/) (2026)
- [KaPQA: Knowledge-Augmented Product Question-Answering](https://mlsanity.com/paper/2407.16073/) (2024)
- [A Large-Language-Model Framework for Automated Humanitarian Situation Reporting](https://mlsanity.com/paper/2512.19475/) (2025)
- [From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents](https://mlsanity.com/paper/2604.01733/) (2026)
- [uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?](https://mlsanity.com/paper/2404.02474/) (2024)
- [QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond](https://mlsanity.com/paper/2404.18371/) (2024)
- [Tripartite-GraphRAG via Plugin Ontologies](https://mlsanity.com/paper/2504.19667/) (2025)
- [Content-based 3D Image Retrieval and a ColBERT-inspired Re-ranking for Tumor Flagging and Staging](https://mlsanity.com/paper/2507.17412/) (2025)
- [GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models](https://mlsanity.com/paper/2509.21593/) (2025)
- [Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrieval](https://mlsanity.com/paper/2510.18659/) (2025)
- [A centroid based framework for text classification in itsm environments](https://mlsanity.com/paper/2511.20667/) (2025)
- [A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search](https://mlsanity.com/paper/2604.16394/) (2026)
- [Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study](https://mlsanity.com/paper/2605.02520/) (2026)
- [Rethinking RAG in Long Videos: What to Retrieve and How to Use It?](https://mlsanity.com/paper/2606.13141/) (2026)
- [Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.29328/) (2026)

## Abstract (arXiv)

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.