Imagine a world where AI could help address the pervasive problem of insomnia. Large Language Models (LLMs), the tech behind AI chatbots, are being explored for their potential in healthcare. However, current research emphasizes the limitations of LLMs in medical contexts, citing their unpredictable nature and the potential for inaccurate or even harmful advice. Researchers are working on ways to make these AI systems safe and reliable. One promising approach involves refining LLM responses by connecting them to specialized, verified medical knowledge bases. Think of it as giving the AI a trusted textbook to draw from, rather than letting it rummage through the entire internet. In addition to curating the information an LLM can access, there's also a new technique being developed called an "actor-critic" prompting protocol. This involves two AI agents: a "Therapist" that provides initial responses to patient queries, and a "Supervisor" that evaluates those responses, ensuring accuracy and appropriateness. This system mimics the way humans learn and reason, with a creative generation phase followed by critical evaluation and error correction. To test this approach, researchers built a "Virtual Sleep Coach" designed to guide users through Cognitive Behavioral Therapy for Insomnia (CBT-I). They then had expert CBT-I therapists blindly evaluate the AI's responses to a series of patient questions. Surprisingly, the AI’s responses often scored higher than those written by the human therapists! This success is likely due to two factors: the AI’s ability to draw on comprehensive CBT-I manuals, and the "Supervisor" AI double-checking the "Therapist" AI’s work. While more research is needed, these early results offer a promising glimpse into the future of AI in healthcare. Imagine AI companions that not only provide accurate information but also offer personalized support, leading to wider access to quality medical care.