Retrieval-augmented generation for interpreting clinical laboratory regulations using large language models.

ABSTRACT: Large language models (LLMs) have demonstrated strong performance on general knowledge tasks, but they have important limitations as standalone tools for question answering in specialized domains where accuracy and consistency are critical. Retrieval-augmented generation (RAG) is a strategy in which LLM outputs are grounded in dynamically retrieved source documents, offering advantages in accuracy, explainability, and maintainability. We developed and evaluated a custom RAG system called Raven, designed to answer laboratory regulatory questions using the part of the Code of Federal Regulations (CFR) pertaining to laboratory (42 CFR Part 493) as an authoritative source. Raven employed a vector search pipeline and a LLM to generate grounded responses via a chatbot-style interface. The system

SUBMITTER: Nanua S 

PROVIDER: S-EPMC12616094 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature