Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report

Understand

Purpose: Large Language Models (LLMs) hold significant promise for medical applications.

• Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs.
• This case study presents the development and evaluation of an LLM-RAG pipeline tailored for healthcare, focusing specifically on preoperative medicine.
• Methods: We developed an LLM-RAG model using 35 preoperative guidelines and tested it against human-generated responses, with a total of 1260 responses evaluated.