While retrieval augmented generation (RAG) has been swiftly adopted in industrial applications based on large language models (LLMs), there is no consensus on what are the best practices for building a RAG system in terms of what are the components, how to organize these components and how to implement each component for the industrial applications, especially in the medical domain. In this work, we first carefully analyze each component of the RAG system and propose practical alternatives for each component. Then, we conduct systematic evaluations on three types of tasks, revealing the best practices for improving the RAG system and how LLM-based RAG systems make trade-offs between performance and efficiency.
An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
LAMIH CNRS/Université Polytechnique Hauts-de-France（LAMIH CNRS/上法兰西理工大学）；CIRRELT；Polytechnique Montréal（蒙特利尔理工学院）；Meedia；Arts et Métiers Institute of Technology（法国国立高等工程技术学院）
A Graph-Retrieval-Augmented Generation Framework Enhances Decision-Making in the Circular Economy
Singapore Institute of Manufacturing Technology (SIMTech)（新加坡制造技术研究所）；Agency for Science, Technology and Research (A*STAR)（科学、技术及研究局）；The University of Sydney（悉尼大学）；Nanyang Technological University（南洋理工大学）；National University of Singapore（国立新加坡大学）
Retrieval-Augmented Generation for Natural Language Processing: A Survey
City University of Hong Kong（香港城市大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；McGill University, Mila（麦吉尔大学，MILA）；National Taiwan University（国立台湾大学）
SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
University of North Dakota（北达科他大学）；Youngstown State University（青年州大学）；University of Toledo（托莱多大学）；University of Missouri（密苏里大学）；Tribhuvan University（特里布文大学）
