In the context of generative AI, a Graph RAG (Retrieval-Augmented Generation) refers to a model that combines a database retrieval mechanism with a language generation model. This approach enhances the generation of text by first retrieving relevant information from a large dataset or knowledge base (sometimes referred to as a knowledge graph) and then using this information to inform the generation process.

The Graph RAG model operates in two main stages:

1. Retrieval Stage: The model queries a dataset to find relevant documents or data points that are pertinent to the prompt. This is typically done using a dense vector search where both the query and the documents are embedded into a high-dimensional space, and the closest documents (in terms of vector distance) are retrieved.
2. Generation Stage: The retrieved documents are then fed into a language generation model, such as a Transformer-based neural network. The generation model uses the context provided by these documents to generate responses that are not only contextually relevant but also rich in detail and factual accuracy.

Graph RAG models are particularly useful in scenarios where generating accurate and informed responses is crucial, such as in question answering systems, chatbots, and other applications requiring interaction with users or synthesis of detailed information. By leveraging both retrieval and generation, these models can produce outputs that are more informative and context-aware compared to traditional generative models that rely solely on pre-trained knowledge embedded during the training phase.