梳理医疗领域RAG系统最佳实践，明确组件选择与优化策略。
Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain
- 拆解RAG各组件，提出可落地的替代方案
- 三类任务测试验证，发现性能与效率的权衡点
- 为工业级医疗RAG系统提供实证指导
尽管基于大语言模型（LLMs）的检索增强生成（RAG）已在工业应用中快速推广，但在医疗领域构建RAG系统时，组件选择、架构设计及实现方式尚无统一标准。本文首先细致分析RAG系统的各个组件，为每个组件提出实用替代方案；随后在三类任务上进行系统性评估，揭示提升RAG性能的最佳实践，并阐明基于LLM的RAG系统在性能与效率之间的权衡关系。
原文摘要 · Abstract (English)
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.
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