This blog post discusses the concept of Graph RAG (Retrieval Augmented Generation) using AWS services and the LlamaIndex framework. It explains how Graph RAG improves upon traditional RAG by utilizing graph databases to provide more structured context for AI language models. The author demonstrates the advantages of Graph RAG over Vector RAG, showing how it can produce more detailed and accurate responses to prompts. The post outlines the process of setting up a Graph RAG system using Amazon Neptune and LlamaIndex, including code snippets and examples. The author also compares Graph RAG and Vector RAG responses to various prompts, highlighting the superior performance of Graph RAG in providing relevant and contextual information.