Enterprises want to utilize Large Language Models (LLMs) in their critical applications, but the unpredictable nature of LLMs can lead to hallucinations or inaccuracies. Retrieval augmented generation is a key consideration for overcoming these challenges by grounding the LLM in facts. Knowledge graphs and vector databases are two primary contenders as potential solutions, but knowledge graphs offer more accurate, reliable, and explainable foundations for LLMs due to their ability to provide precise information based on traversing connected relationships. While vector databases can connect factual pieces of information together, they often rely on similarity scoring and predefined limits, leading to incomplete or irrelevant results. Knowledge graphs, with their human-readable representation of data, offer full transparency and the ability to identify misinformation, making them a better choice for backing LLMs in mission-critical applications.