Imagine asking your doctor a complex medical question and receiving an instant, comprehensive answer backed by scientific evidence. That's the promise of large language models (LLMs) in healthcare. But what if these AI assistants start hallucinating facts or citing non-existent studies? This is a critical challenge researchers are tackling, particularly in specialized fields like ophthalmology. A recent study explores how to make LLMs more reliable by augmenting their responses with real, evidence-based medical information. Researchers built a system that feeds ophthalmology-specific documents, including journal articles, guidelines, and educational resources, to LLMs. Think of it as giving the AI a comprehensive medical textbook to consult before answering your questions. The study tested this approach with 100 real-world questions from patients. While the AI was reasonably accurate without the extra information, it often hallucinated references, making it hard to trust the answers completely. By incorporating relevant medical literature, the system significantly improved the AI's ability to cite real evidence. Interestingly, the AI didn’t always select the most relevant documents provided, sometimes still hallucinating or missing key information. This highlights the ongoing challenge of teaching AI to reason like a doctor. While this research shows promising results, it also reveals the complex task of building trustworthy medical AI. The next steps involve refining the document retrieval and selection process, and testing different LLM architectures. The ultimate goal? To develop AI systems that empower both patients and healthcare providers with accurate, reliable, and easily accessible medical information.