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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Performance Modeling of Lightweight Retrieval-Augmented Large Language Models for Low-Resource Plastic Surgery

Nora Y Sun^1,2, Ariana Genovese¹, Srinivasagam Prabha¹

¹Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.

Bioengineering (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

Retrieval-augmented generation (RAG) systems can improve large language model (LLM) reliability for surgeons. Configuration, not model size, drives performance in plastic surgery applications.

Keywords:

artificial intelligence large language models plastic surgery retrieval-augmented generation

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Published on: June 13, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Surgical Education Technology

Background:

Large language models (LLMs) offer potential for surgical education but face challenges with accuracy and reliability.
Retrieval-augmented generation (RAG) aims to improve LLM accuracy by grounding responses in external knowledge bases.
The performance impact of various RAG configurations, especially concerning computational costs, is not well understood.

Purpose of the Study:

To evaluate the performance of lightweight, open-source Retrieval-Augmented Generation (RAG) configurations for plastic surgery applications.
To identify key factors influencing RAG performance across different query complexities and subspecialties.
To determine optimal RAG configurations for reliable LLM-assisted surgical reference.

Main Methods:

Evaluated 120 lightweight, open-source RAG configurations using 40 plastic surgery QA tasks (single- and multi-hop).
Varied base LLMs (Phi-3-mini-128k-instruct, BioMistral-7B), embedding models, database sizes, and chunk sizes.
Assessed performance via semantic similarity (Ragas) to physician-validated answers, analyzed using linear mixed-effects regression.

Main Results:

Lightweight, open-source RAG models can achieve high performance.
Phi-3-mini-128k-instruct showed consistent performance across query types, while BioMistral-7B excelled in specific configurations.
Larger database sizes and specific embedding models (bge-large-en-v1.5) improved performance; multi-hop queries decreased it, though less so for Phi-3-mini-128k-instruct.

Conclusions:

Effective RAG systems for plastic surgery do not necessitate large proprietary models; performance hinges on configuration choices.
Interaction effects between components significantly influence RAG system outcomes.
Future advancements in predictive modeling could facilitate resource-efficient and safe clinical RAG deployment.