Retrieval-augmented generation (RAG) and GraphRAG both address the limitation of large language models (LLMs) in accessing up-to-date, connected knowledge by incorporating external information into their responses. While RAG uses semantic retrieval to inject relevant data from external sources into a model's prompt, GraphRAG extends this by adding a layer of structure and reasoning with a knowledge graph that captures relational context. This enables GraphRAG to understand how pieces of information are interconnected, allowing it to perform multi-hop reasoning and track dependencies, making it suitable for complex tasks like supply chain analysis and healthcare intelligence. RAG is ideal for scenarios requiring quick setup and handling unstructured text, such as customer support and document Q&A, but it falls short in reasoning about relationships, often resulting in fragmented context and over-retrieval. GraphRAG builds on RAG's foundation by offering a more dynamic retrieval approach, integrating semantic, text, or hybrid search methods to locate relevant entry points before expanding context through graph reasoning. This makes GraphRAG not a replacement but an evolution of RAG, as it blends the speed of RAG with the relational intelligence of graph reasoning, creating smarter AI systems that understand both facts and their interconnections.