Martin O'Hanlon

Martin O'Hanlon - Make LLMs make sense with GraphRAG

How do you stop LLMs from making things up? By connecting them to a knowledge graph that acts as their factual 'left brain'.

Martin O'Hanlon - Make LLMs make sense with GraphRAG
#1about 2 minutes

Understanding the problem of LLM hallucinations

Large language models are powerful but often invent facts, a problem known as hallucination, which presents made-up information as truth.

#2about 5 minutes

Demonstrating how context can ground LLM responses

A live demo in the OpenAI playground shows how an LLM hallucinates a weather report but provides a factual response when given context.

#3about 2 minutes

Introducing retrieval-augmented generation (RAG)

Retrieval-augmented generation is an architectural pattern that improves LLM outputs by augmenting the prompt with retrieved, factual information.

#4about 5 minutes

Understanding the fundamentals of graph databases

Graph databases like Neo4j model data using nodes for entities, labels for categorization, and relationships to represent connections between them.

#5about 6 minutes

Using graphs for specific, fact-based queries

While vector embeddings are good for fuzzy matching, knowledge graphs excel at providing context for highly specific, fact-based questions.

#6about 3 minutes

Demonstrating GraphRAG with a practical example

A live demo shows how adding factual context from a knowledge graph, such as a beach closure, dramatically improves the LLM's recommendation.

#7about 2 minutes

Summarizing the two main uses of GraphRAG

GraphRAG serves two key purposes: extracting entities from unstructured text to build a knowledge graph and using that graph to provide better context for LLMs.

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