Martin O'Hanlon
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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