Large Language Models (LLMs) have revolutionized natural language processing, yet challenges persist in accuracy and explainability. This talk introduces GraphRAG, an innovative framework that integrates Knowledge Graphs (KGs) with LLMs to address these issues. By leveraging the rich, structured data from KGs, GraphRAG refines LLM outputs, providing a robust context that enhances the precision of generated information. This integration not only improves the factual accuracy of responses but also significantly boosts model interpretability. We will demonstrate how GraphRAG utilizes the interconnected nature of KGs to ground LLMs in real-world knowledge, allowing for more reliable and transparent AI systems. Through practical examples and performance evaluations, attendees will see how this approach mitigates common limitations of traditional LLMs, such as ambiguity and information gaps. The session will highlight advancements in both the effectiveness and clarity of AI responses, showcasing the transformative impact of combining Graph Technology with LLMs. Join us to explore how GraphRAG is setting a new standard in enhancing AI's accuracy and explainability.