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.

Related jobs
Jobs that call for the skills explored in this talk.

Matching moments

Using Graph RAG for superior context retrieval
03:35 MIN

Using Graph RAG for superior context retrieval

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

Understanding retrieval-augmented generation (RAG)
05:31 MIN

Understanding retrieval-augmented generation (RAG)

Exploring LLMs across clouds

Comparing LLM, vector search, and graph RAG approaches
03:45 MIN

Comparing LLM, vector search, and graph RAG approaches

Give Your LLMs a Left Brain

Using knowledge graphs to give LLMs a left brain
03:01 MIN

Using knowledge graphs to give LLMs a left brain

Give Your LLMs a Left Brain

Using RAG to enrich LLMs with proprietary data
03:17 MIN

Using RAG to enrich LLMs with proprietary data

RAG like a hero with Docling

Understanding LLMs, context windows, and RAG
04:12 MIN

Understanding LLMs, context windows, and RAG

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

Solving LLM limitations with RAG and vector databases
01:48 MIN

Solving LLM limitations with RAG and vector databases

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

Why large language models need retrieval augmented generation
03:29 MIN

Why large language models need retrieval augmented generation

Build RAG from Scratch

Featured Partners

Related Videos

See all videos
How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge42:26

How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge

Meta Atamel & Guillaume Laforge

Lies, Damned Lies and Large Language Models29:40

Lies, Damned Lies and Large Language Models

Jodie Burchell

Creating Industry ready solutions with LLM Models58:00

Creating Industry ready solutions with LLM Models

Vijay Krishan Gupta & Gauravdeep Singh Lotey

Large Language Models ❤️ Knowledge Graphs29:11

Large Language Models ❤️ Knowledge Graphs

Michael Hunger

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j46:51

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

Give Your LLMs a Left Brain27:35

Give Your LLMs a Left Brain

Stephen Chin

Building Blocks of RAG: From Understanding to Implementation26:25

Building Blocks of RAG: From Understanding to Implementation

Ashish Sharma

Knowledge graph based chatbot24:44

Knowledge graph based chatbot

Tomaz Bratanic

Related Articles

View all articles
CH
Chris Heilmann
With AIs wide open - WeAreDevelopers at All Things Open 2025
Last week our VP of Developer Relations, Chris Heilmann, flew to Raleigh, North Carolina to present at All Things Open . An excellent event he had spoken at a few times in the past and this being the “Lucky 13” edition, he didn’t hesitate to come and...
With AIs wide open - WeAreDevelopers at All Things Open 2025
DC
Daniel Cranney
Dev Digest 160: Graphs and RAGs Explained and VS Code Extension Hacks
Inside last week’s Dev Digest 160 . 🤖 How AI is reshaping UI and work 🚀 Tips on how to use Cursor most efficiently 🔒 How VS Code extensions can be a massive security issue 👩‍💻 What the move to Go for Typescript means for developers 👎 What a possible...
Dev Digest 160: Graphs and RAGs Explained and VS Code Extension Hacks
MH
Michael Hunger, Neo4j
Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?
After a few intense years of building with AI, many teams are asking the same question: Where is the ROI? We’re shipping chatbots, copilots, and agents . But: Agents aren’t autonomous enough to own end‑to‑end workflows. Model quality degrades over t...
Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?
LM
Luis Minvielle
What Are Large Language Models?
Developers and writers can finally agree on one thing: Large Language Models, the subset of AIs that drive ChatGPT and its competitors, are stunning tech creations. Developers enjoying the likes of GitHub Copilot know the feeling: this new kind of te...
What Are Large Language Models?

From learning to earning

Jobs that call for the skills explored in this talk.