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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Matching moments

Using Graph RAG for superior context retrieval
08:58 MIN

Using Graph RAG for superior context retrieval

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j

Understanding retrieval-augmented generation (RAG)
15:49 MIN

Understanding retrieval-augmented generation (RAG)

Exploring LLMs across clouds

Understanding LLMs, context windows, and RAG
00:53 MIN

Understanding LLMs, context windows, and RAG

Beyond Prompting: Building Scalable AI with Multi-Agent Systems and MCP

Why large language models need retrieval augmented generation
00:57 MIN

Why large language models need retrieval augmented generation

Build RAG from Scratch

Mitigating LLM hallucinations with RAG
17:31 MIN

Mitigating LLM hallucinations with RAG

From ML to LLM: On-device AI in the Browser

Understanding Retrieval-Augmented Generation (RAG)
06:05 MIN

Understanding Retrieval-Augmented Generation (RAG)

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j

How RAG provides LLMs with up-to-date context
01:32 MIN

How RAG provides LLMs with up-to-date context

How to scrape modern websites to feed AI agents

How RAG solves LLM limitations
08:01 MIN

How RAG solves LLM limitations

Building Blocks of RAG: From Understanding to Implementation

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From learning to earning

Jobs that call for the skills explored in this talk.

Generative AI Developer

Generative AI Developer

University of the Arts, London
Sleaford, United Kingdom

£34-41K
Python
PyTorch
TensorFlow