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.

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Matching moments

Understanding the problem of LLM hallucinations
00:02 MIN

Understanding the problem of LLM hallucinations

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Shifting from general LLMs to specialized models
30:39 MIN

Shifting from general LLMs to specialized models

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Understanding the dual nature of large language models
00:07 MIN

Understanding the dual nature of large language models

Lies, Damned Lies and Large Language Models

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

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

Addressing the core challenges of large language models
00:27 MIN

Addressing the core challenges of large language models

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

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

Understanding retrieval-augmented generation (RAG)

Exploring LLMs across clouds

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

Why large language models need retrieval augmented generation

Build RAG from Scratch

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