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.

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

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

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

Introducing retrieval-augmented generation to improve accuracy
03:23 MIN

Introducing retrieval-augmented generation to improve accuracy

Knowledge graph based chatbot

Powering real-time AI with retrieval augmented generation
08:26 MIN

Powering real-time AI with retrieval augmented generation

Scrape, Train, Predict: The Lifecycle of Data for AI Applications

Using RAG to enrich LLMs with proprietary data
00:40 MIN

Using RAG to enrich LLMs with proprietary data

RAG like a hero with Docling

Overcoming the problem of stale knowledge in LLMs
09:37 MIN

Overcoming the problem of stale knowledge in LLMs

Engineering Mindset in the Age of AI - Gunnar Grosch, AWS

Understanding retrieval-augmented generation systems
03:31 MIN

Understanding retrieval-augmented generation systems

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

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