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.

#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