GraphAcademy Live: Neo4j & LLM Fundamentals

This podcast episode presents an insightful journey through the integration of Neo4j with generative AI, examining how knowledge graphs enhance Large Language Models (LLMs) to improve context and accuracy. The hosts guide listeners from foundational concepts, such as graph databases and semantic search, to practical applications with Langchain and Retrieval Augmented Generation (RAG). The dialogue underscores the importance of prompt engineering and real-time data grounding to counteract the pitfalls of LLM "hallucinations," offering a clear understanding of how graph technology can complement AI in producing more informed and reliable outputs.

Outlines

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Neo4j

Introduction to Neo4j and LLM Fundamentals Course

Understanding Knowledge Graphs and Large Language Models (LLMs)

Techniques for Avoiding LLM Hallucinations and Introduction to RAG

Vectors, Semantic Search, and Vector Indexing in Neo4j

Introduction to Langchain and Building LLM Applications

GraphAcademy Live: Neo4j & LLM Fundamentals

Neo4j

00:02Introduction to Neo4j and LLM Fundamentals Course

Introduction to Neo4j and LLM Fundamentals Course

06:34Understanding Knowledge Graphs and Large Language Models (LLMs)

Understanding Knowledge Graphs and Large Language Models (LLMs)

15:15Techniques for Avoiding LLM Hallucinations and Introduction to RAG

Techniques for Avoiding LLM Hallucinations and Introduction to RAG

28:34Vectors, Semantic Search, and Vector Indexing in Neo4j

Vectors, Semantic Search, and Vector Indexing in Neo4j

47:03Introduction to Langchain and Building LLM Applications

Introduction to Langchain and Building LLM Applications