Michael Hunger

Large Language Models ❤️ Knowledge Graphs

LLMs are trained to be helpful, not factual. Learn to stop hallucinations by grounding them in a knowledge graph, creating AI you can actually trust.

Large Language Models ❤️ Knowledge Graphs
#1about 3 minutes

Addressing the key challenges of large language models

LLMs often hallucinate or lack access to private data because they are trained to be helpful, not necessarily factual.

#2about 2 minutes

Using Retrieval Augmented Generation to ground LLMs

The RAG pattern improves LLM accuracy by first retrieving relevant information from a database to provide as context for the answer.

#3about 4 minutes

Representing complex data with knowledge graphs

Knowledge graphs model data as a network of entities and relationships, making complex connections intuitive and easy to query.

#4about 3 minutes

Using LLMs to build a knowledge graph from text

LLMs can automatically extract structured entities and relationships from unstructured documents to populate a knowledge graph.

#5about 3 minutes

Demo of extracting conference data into a graph

An application ingests a conference agenda and uses an LLM to automatically build a knowledge graph of speakers and their talks.

#6about 3 minutes

Combining vector and graph search with GraphRAG

The GraphRAG pattern uses vector search to find entry points into the graph and then traverses relationships to gather richer, more relevant context.

#7about 5 minutes

Code demo of querying a graph with LangChain

A Jupyter notebook demonstrates how to use LangChain and Neo4j to execute a GraphRAG query that avoids LLM hallucinations.

#8about 3 minutes

Benefits and traceability of the GraphRAG approach

This approach provides rich context, enables explainability by tracing data sources, and allows for graph enrichment with clustering algorithms.

#9about 2 minutes

How to control and validate graph extraction quality

You can guide the LLM's extraction process with a predefined schema and validate its output against a human-created baseline for accuracy.

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Matching moments

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

Understanding retrieval-augmented generation (RAG)

Exploring LLMs across clouds

Introducing retrieval-augmented generation (RAG)
07:24 MIN

Introducing retrieval-augmented generation (RAG)

Martin O'Hanlon - Make LLMs make sense with GraphRAG

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

Why large language models need retrieval augmented generation

Build RAG from Scratch

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

Demonstrating GraphRAG with a practical example
20:24 MIN

Demonstrating GraphRAG with a practical example

Martin O'Hanlon - Make LLMs make sense with GraphRAG

How RAG solves LLM limitations
08:01 MIN

How RAG solves LLM limitations

Building Blocks of RAG: From Understanding to Implementation

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

Understanding Retrieval-Augmented Generation (RAG)

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

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

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