Large language models (LLMs) have recently captured significant attention in healthcare. Their stellar performance across a range of applications highlights their potential for vital tasks like medical question-answering. However, LLMs can produce "hallucination" errors, generating overly confident answers without sufficient factual basis. We'll provide a detailed walkthrough on how to set up, deploy, and enhance the trustworthiness of a retrieval augmented generation (RAG) pipeline for building clinical question-answering systems. This approach ensures LLMs rely on specific evidence sets when used in automated clinical question-answering systems. We'll guide you through the three primary stages of developing this pipeline.
Prerequisite(s):
Familiarity with PyTorch and natural language processing concepts
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