We present two innovative applications of Large Language Models (LLMs), at Amazon Pharmacy: (1) an AI assistant for customer support and (2) a medication direction copilot for patient safety. The Pharmacy AI Assistant, launched in March 2024 based on LLM and RAG, led to an 11% reduction in human support contact rate and a 15% improvement in issue resolution rates. We later introduced a new feature: a hybrid architecture integrating multi-armed bandits with LLMs to dynamically suggest follow-up questions, addressing challenges in customer inquiry articulation while balancing exploration and exploitation in conversational flows, which led to an additional 9% reduction in contact rate; (2) MEDIC (medication direction copilot), a system that emulates pharmacist reasoning by fine-tuning an LLM with a compact set of expert-annotated directions to accurately extract and communicate core prescription components. When compared against two state-of-the-art LLM-based benchmarks, competitors, they recorded 1.51 and 4.38 times more near-miss events (errors caught before reaching our patients) than MEDIC. Production deployment demonstrated a 33% reduction in medication direction errors. These systems demonstrate how LLMs, when enhanced with domain expertise and strategic architectural decisions, can significantly improve both customer experience and patient safety in online pharmacy operations.