In this talk, you will learn about GraphRAG, a technique that combines graph databases with generative AI to improve the quality of LLM-generated content. We will explore the terms Retrieval-Augmented Generation (RAG) and Context Engineering, and how GraphRAG can be used in both scenarios. The topic is aimed at Generative AI practitioners who are familiar with vector-based Retrieval-Augmented Generation (RAG) and would like to understand how the approach of GraphRAG can improve the quality of LLM-generated content.
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Speaker
Irina Loghin
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talks
Developer Relations
Neo4j
Irina Loghin is a Developer Relations professional at Neo4j. She will discuss GraphRAG, a technique that combines graph databases with generative AI to improve the quality of LLM-generated content, including Retrieval-Augmented Generation (RAG) and Context Engineering.
Bio from: GenAI Loft London - Day 6: Code. Scale. Disrupt
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