Stephen Chin

Give Your LLMs a Left Brain

LLMs are creative right-brains, but they hallucinate. Give your model a logical left-brain by connecting it to a knowledge graph for factual, auditable, and trustworthy answers.

Give Your LLMs a Left Brain
#1about 3 minutes

The challenge of applying general LLMs to enterprise problems

Large language models trained on general data face significant obstacles when applied to specific enterprise contexts and problems.

#2about 7 minutes

Demonstrating LLM hallucinations with tricky questions

LLMs can produce incorrect or nonsensical answers, known as hallucinations, when faced with questions that combine math, reasoning, and inherent biases.

#3about 4 minutes

Why LLMs are creative but not always factual

LLMs operate on word vectors and probabilistic transformers to predict the next word, making them excellent storytellers but poor at factual reasoning.

#4about 3 minutes

Using knowledge graphs to give LLMs a left brain

Pairing LLMs with knowledge graphs built from factual enterprise data provides the logical, sequential thinking needed for reliable results.

#5about 4 minutes

Comparing LLM, vector search, and graph RAG approaches

While vector databases add private data context, combining them with knowledge graphs provides superior domain understanding, precision, and explainability.

#6about 2 minutes

An architecture for integrating knowledge graphs with LLMs

A practical implementation pattern routes queries through a knowledge graph and vector database to provide enriched context to the LLM for more accurate answers.

#7about 1 minute

Enabling governance and explainability with knowledge graphs

Knowledge graphs allow for granular data governance and provide the ability to trace and audit LLM results back to their source nodes.

#8about 3 minutes

Resources for learning to build with knowledge graphs

Continue learning how to combine LLMs and knowledge graphs with recommended courses on DeepLearning.AI and Graph Academy.

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

Matching moments

Addressing the core challenges of large language models
05:18 MIN

Addressing the core challenges of large language models

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

Understanding the problem of LLM hallucinations
02:29 MIN

Understanding the problem of LLM hallucinations

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Shifting from general LLMs to specialized models
02:30 MIN

Shifting from general LLMs to specialized models

ChatGPT vs Google: SEO in the Age of AI Search - Eric Enge

Addressing the key challenges of large language models
02:55 MIN

Addressing the key challenges of large language models

Large Language Models ❤️ Knowledge Graphs

Understanding the dual nature of large language models
02:18 MIN

Understanding the dual nature of large language models

Lies, Damned Lies and Large Language Models

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

Securely connecting generative AI to enterprise data
02:32 MIN

Securely connecting generative AI to enterprise data

How E.On productionizes its AI model & Implementation of Secure Generative AI.

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

Understanding retrieval-augmented generation (RAG)

Exploring LLMs across clouds

Featured Partners

Related Videos

See all videos
Large Language Models ❤️ Knowledge Graphs
29:11

Large Language Models ❤️ Knowledge Graphs

Michael Hunger

Martin O'Hanlon - Make LLMs make sense with GraphRAG
24:42

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Martin O'Hanlon

Knowledge graph based chatbot
24:44

Knowledge graph based chatbot

Tomaz Bratanic

Inside the Mind of an LLM
27:11

Inside the Mind of an LLM

Emanuele Fabbiani

Creating Industry ready solutions with LLM Models
58:00

Creating Industry ready solutions with LLM Models

Vijay Krishan Gupta & Gauravdeep Singh Lotey

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation
21:17

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Carl Lapierre

Exploring LLMs across clouds
28:38

Exploring LLMs across clouds

Tomislav Tipurić

Lies, Damned Lies and Large Language Models
29:40

Lies, Damned Lies and Large Language Models

Jodie Burchell

Related Articles

View all articles
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?
Krissy Davis
The Best Large Language Models on The Market
Large language models are sophisticated programs that enable machines to comprehend and generate human-like text. They have been the foundation of natural language processing for almost a decade. Although generative AI has only recently gained popula...
The Best Large Language Models on The Market
Daniel Cranney
Stephan Gillich - Bringing AI Everywhere
In the ever-evolving world of technology, AI continues to be the frontier for innovation and transformation. Stephan Gillich, from the AI Center of Excellence at Intel, dove into the subject in a recent session titled "Bringing AI Everywhere," sheddi...
Stephan Gillich - Bringing AI Everywhere

From learning to earning

Jobs that call for the skills explored in this talk.