Graph RAG is an advanced technique in the field of Generative AI that combines Retrieval-Augmented Generation (RAG) with Graph data structures for better retrieval performance. In this approach, a graph structure is utilized to organize and retrieve relevant information from a large corpus of data, which is then used to inform and guide the generation process of a Large Language Model (LLM). The method leverages the strengths of both retrieval and generation, allowing for more contextually accurate and coherent outputs. By integrating graph-based retrieval mechanisms, Graph RAG can efficiently handle complex queries and provide more precise responses, making it particularly useful in applications requiring detailed and context-rich information synthesis.
The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI.
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