This study presents the design and implementation of a safety-constrained large language model (LLM) system for public health information access, focusing on maternal and child health (MCH) resource navigation. While LLM-based systems provide flexible and natural interfaces for information retrieval, their deployment in healthcare contexts introduces risks related to safety, trust, and uncontrolled generation.
We explore practical design patterns for constraining LLM behavior in safety-critical environments. A multi-layered architecture is introduced, integrating domain-restricted retrieval augmented generation (RAG), strict boundary enforcement to prevent medical advice, anonymous multi-user session management, and comprehensive audit logging for monitoring and compliance. A key aspect of the design is a controlled data pipeline that grounds all responses in curated public health resources, avoiding reliance on the model's pretrained medical knowledge.
The system was implemented in a real-world public health setting, and scenario-based validation was conducted across in-scope, out-of-scope, and emergency queries. Results show consistent enforcement of safety constraints, reliable resource grounding, and stable system performance, with an average response time of 5.3 seconds. Beyond the specific application, we discuss design trade-offs and lessons learned in balancing safety, usability, and system flexibility. Our findings provide practical guidance for deploying LLM-based systems in healthcare and other domains where strict information boundaries and accountability are required.
Blogger's Review: This paper delves into the safe deployment of large language models in public health, emphasizing the importance of a multi-layered architecture and safety constraints. It not only serves as a reference for health information retrieval but also offers valuable insights for system design in other fields. The rigorous validation process ensures reliability and safety, making it a noteworthy contribution to the discussion.