Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain

2026-02-03

AI Summary

This study addresses the lack of standardized guidelines for deploying retrieval-augmented generation (RAG) systems in industrial healthcare settings. It presents the first systematic evaluation of RAG components on medical tasks, employing a modular architecture, ablation studies, and multi-task assessment to investigate the trade-offs between performance and efficiency. The work proposes a practical, evidence-based framework of best practices and identifies an optimal component configuration that significantly improves both answer accuracy and reasoning efficiency across three representative medical tasks. These findings provide empirical support and actionable technical guidance for the real-world deployment of medical RAG systems.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Retrieval-Augmented Generation
• Medical Domain
• Best Practices
• Large Language Models
• Industrial Applications

Innovation

• Retrieval-Augmented Generation
• Best Practices
• Medical Domain
• Large Language Models
• Systematic Evaluation