Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914323895418880 |
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| author | Li, Liz Zhu, Wei |
| author_facet | Li, Liz Zhu, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03368 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain Li, Liz Zhu, Wei Computation and Language 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. |
| title | Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.03368 |