The paper investigates configuration problems in Retrieval-Augmented Generation (RAG) systems, which combine large language models with components like vector or graph databases. An empirical study of 654 real-world issues from 10 RAG systems categorizes symptoms, root causes, stages, and fix patterns, revealing that symptoms often appear early or late in the pipeline while root causes may lie elsewhere. The authors also offer nine actionable insights for practitioners to test, localize, and resolve configuration issues in RAG systems.
arXiv:2412. 15529v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current.
By Qili Zhang, Qianren Mao, Yangyifei Luo, Yashuo Luo, Hanwen Hao, Zhilong Cao, Weifeng Jiang, Zhijun Chen, Junnan Liu, Feng Yan, Xiaolong Wang, Jinlong Zhang, Zhenting Huang, Zhixing Tan, Jie Sun, Bo Li, Jianxin Li, Philip S. Yu
The paper introduces a conceptual framework to classify and evaluate Retrieval-Augmented Generation (RAG) techniques, addressing the challenge of selecting suitable methods for specific tasks. It identifies main dimensions and sub-dimensions based on a qualitative analysis of existing literature. The framework can act as a pre-empirical filter, helping researchers and practitioners choose the most fitting RAG candidates before costly benchmarking.
By Emmanuel Igbekele, Olawande Daramola
arXiv:2610.27816v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding the outputs of large language models (LLM) in external knowledge....
By Niclas Hart, Nicolas Weeger
arXiv:2606. 29377v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile.
By Soroush Hashemifar, Havva Alizadeh Noughabi, Fattane Zarrinkalam, Ali Dehghantanha
arXiv:2608. 03487v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG), which augments large language model (LLM) generation with information retrieved from databases, has become a widely used approach for knowledge-intensive applications.
By Haiqiang Zhang, Yuanqing Lei, Wanting Li, Tao Zhang, Wenqi Jiang
arXiv:2609.37669v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding p...
By Sabrina Kaniewski, Tim Kr\"amer, Julius B\"achle, Markus Enzweiler, Michael Menth, Tobias Heer
arXiv:2609.36700v1 Announce Type: cross
Abstract: When conversing with large language models (LLMs), users often begin with a simple question and build towards a multi-hop question through follow-up...
By Pranav Handa, Ariful Azad
arXiv:2608. 16394v1 Announce Type: new Abstract: Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long, hierarchical standards.
By Vahid Zolfaghari, Nenad Petrovic, Andr\'E Schamschurko, Alois Knoll
arXiv:2606. 30062v1 Announce Type: cross Abstract: While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention.
By Dari Baturova, Elena Bruches, Ivan Chernov, Roman Derunets, Arsenii Fomin, Andrey Kostin
arXiv:2608.21558v1 Announce Type: cross
Abstract: Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess...
By Lorenz Brehme, Adam Jatowt
While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention. In this study, we investigate how smaller language models perform during the generation stage within a Retrieval-Augmented Generation (RAG) system.