Retrieval-Augmented Generation (RAG) combines traditional information retrieval systems with large language models (LLMs) to enhance generative AI by integrating external knowledge sources, resulting in more accurate and relevant responses. RAG processes involve retrieving, pre-processing, and integrating external data to enrich context for LLMs, thereby improving response quality. Despite its advantages, baseline RAG faces limitations in synthesizing disparate information and understanding large datasets. To address these challenges, Microsoft Research introduced GraphRAG, which constructs dynamic knowledge graphs to organize and connect information hierarchically, enhancing the ability to answer complex queries. GraphRAG improves accuracy and contextual understanding by structuring data, facilitating better reasoning over intricate queries, and refining information retrieval processes. While GraphRAG offers deeper insights and improved problem-solving capabilities, it incurs higher computational costs due to increased LLM calls. The choice between GraphRAG and traditional RAG depends on specific use cases and the complexity of queries, with GraphRAG excelling in multi-step, context-rich scenarios and traditional RAG being more efficient for straightforward tasks.