Retrieval-Augmented Generation (RAG) combines retrieval-based methods with generative language models to enhance response quality and accuracy. By fetching relevant information from a database, RAG ensures generated responses are contextually relevant and factually correct.
In neo4j-graphrag, RAG leverages the graph database to retrieve nodes and relationships that inform the language model’s output, significantly improving the quality of responses from large language models (LLMs).
The core of RAG is in its ability to search effectively—finding the right information at the right time and using it to drive accurate, generative outputs. This combination of retrieval and generation represents a fundamental advancement in providing reliable AI-driven insights.
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Summary
You learned about Retrieval-Augmented Generation (RAG) and the purpose of the neo4j-graphrag project.