How to Build a RAG System for Healthcare: Minimize Hallucinations in LLM Outputs

Author(s): Marie Humbert-Droz, PhD

Originally published on Towards AI.

Building Trustworthy Healthcare LLM Systems — Part 2

In our previous post, we explored why hallucinations occur in Large Language Models and the particular risks they pose in healthcare settings. We also set up a process to download relevant medical literature from PubMed Central to build our knowledge base. Now, let’s transform that medical corpus into a functional Retrieval-Augmented Generation (RAG) system to minimize hallucinations in healthcare applications.

The article elaborates on the construction of a Retrieval-Augmented Generation (RAG) system for healthcare, aimed at mitigating hallucinations by effectively incorporating external knowledge sources and verified medical literature. It details the implementation steps, including document processing, embedding creation, and setting up a query engine using specific frameworks like LlamaIndex. The discussion includes chunking strategies, specialized medical embedding options, and relevant performances metrics for evaluating the RAG system’s effectiveness. Finally, it addresses the challenges faced and underscores the need for continuous improvement in building trustworthy healthcare AI systems.

Read the full blog for free on Medium.

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Published via Towards AI