Hallucination-free AI zone: LLMs + Graph DBs + Java!

Hallucinations in AI occur when models generate plausible-sounding but incorrect information. For Java developers working with Large Language Models (LLMs), this presents a risk: the model may confidently provide wrong answers, creating issues in applications where accuracy is critical. By integrating a graph database, however, you can anchor the LLM in reality. A graph database allows you to map relationships between entities, preventing false outputs and securing sensitive data, making it an invaluable tool for improving the reliability of AI-driven systems.

In this presentation, we’ll explore how graph databases, with their node-and-edge structure, offer significant advantages over traditional databases. Java developers will see how this structure is ideal for tasks like knowledge graphs or fraud detection. We’ll also cover how AI tools can leverage graph databases to eliminate LLM hallucinations and improve security. Plus, we’ll explain why vector indexes can outperform traditional vector databases, delivering smarter, faster, and more accurate results.

Thanks to Microsoft for hosting

Thanks to Neo4J for the pizza

Jennifer Reif

Jennifer Reif is a Developer Relations Engineer at Neo4j, conference speaker, blogger, and an avid developer and problem-solver. She holds a Master’s degree in Computer Management and Information Systems. She has worked with large enterprises to organize and make sense of widespread data assets and leverage them for maximum business value. She has worked with a variety of commercial and open-source tools and enjoys learning new technologies, sometimes daily! Her passion is finding ways to organize chaos and deliver software more effectively.