mlsanityData to 7 Oct 2026

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

arXiv 2602.03368 ↗First posted 3 Feb 2026cs.CLcited by 1

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 42 of its 92 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

42 of 92 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine23 Jan
  2. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb
  3. 2023Almanac: Retrieval-Augmented Language Models for Clinical Medicine1 Mar

Other references · 39

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  6. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
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  10. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  11. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  12. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 996
27 more
  1. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  2. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  3. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  4. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  5. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  6. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
  7. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  8. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 357
  9. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  10. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
  11. 2024Capabilities of Gemini Models in Medicinecited by 231
  12. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 182
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  17. 2024AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learningcited by 33
  18. 2021Literature-Augmented Clinical Outcome Predictioncited by 20
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  20. 2023Search Still Matters: Information Retrieval in the Era of Generative AIcited by 17
  21. 2025AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent Systemcited by 15
  22. 2023Overview of the PromptCBLUE Shared Task in CHIP2023cited by 14
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  27. 2025FT-MDT: Extracting Decision Trees from Medical Texts via a Novel Low-rank Adaptation Methodcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  4. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
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