Safety-Constrained LLM System for Public Health Information Access

Jul 16, 2026

A preprint describes a multi-layered large language model (LLM) system designed for maternal and child health resource navigation. The system integrates domain-restricted retrieval augmented generation (RAG), strict boundary enforcement to prevent medical advice, anonymous session management, and audit logging. Scenario-based validation demonstrates consistent enforcement of safety constraints, reliable grounding in curated resources, and an average response time of 5.3 seconds.

Why it matters: This work offers practical design patterns for safely deploying LLMs in healthcare and other safety-critical domains requiring strict information boundaries and accountability.

Full story at: arXiv AI/ML ↗