Stephen Chin
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
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