This presentation explores how healthcare chatbot accuracy can be significantly improved through the implementation of John Snow Labs’ Medical LLM‑Medium as an evaluation mechanism in retrieval augmented generation (RAG) systems. We demonstrate how this “LLM‑as‑a‑Judge” approach provides automated, consistent assessment of critical information retrieval and generation metrics including relevancy, coherence, and correctness.By leveraging specialized medical language models to evaluate RAG outputs, organizations can implement more rigorous quality control at scale without the time and expense of human evaluation. The John Snow Labs solution scores aspects of the RAG pipelines with transparent rubrics and natural language explanations, making assessment results interpretable for both technical and non‑technical stakeholders. This approach shortens feedback loops, accelerates improvement cycles, and provides greater confidence when deploying healthcare chatbots in production environments where accuracy is paramount.The presentation will showcase practical implementation strategies and real‑world results that demonstrate how this evaluation framework enables healthcare organizations to build more reliable, trustworthy AI‑powered applications while maintaining compliance with healthcare information standards.