article provides an overview of advanced Retrieval-Augmented Generation (RAG) techniques used to improve the performance and accuracy of large language models (LLMs). It describes how to use techniques such as

pre-retrieval, retrieval, post-retrieval, and generation to address the challenges of Naive RAG, including inaccurate results and slow response times. The article specifically focuses on how knowledge graphs and GraphRAG can be used to optimize RAG applications. It concludes by highlighting the benefits of FalkorDB, a specialized database designed for advanced RAG techniques, particularly with its knowledge graph support and seamless integration with LLMs.