Large Language Models' propensity for generating factually incorrect content, known as "hallucinations," poses significant risks in high-stakes domains. To address this limitation, researchers have developed a domain-grounded tiered retrieval and verification architecture1. This framework is designed to systematically identify and correct factual inaccuracies, thereby enhancing the reliability of LLMs. By grounding the retrieval process in specific domains, the architecture can better distinguish between accurate and inaccurate information. The tiered approach enables a more nuanced evaluation of generated content, reducing the likelihood of hallucinations. This innovation has significant implications for the development of more trustworthy AI systems, particularly in domains where accuracy is paramount. The ability to mitigate hallucinations in LLMs matters to practitioners because it can help prevent the dissemination of misinformation and promote more reliable decision-making in critical areas such as policy, security, and workforce management.