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Hallucination-free zone: LLMs + Graph Databases got your back!

Hallucinations refer to the generation of contextually plausible but incorrect or fabricated information, demonstrating the model’s capacity to produce imaginative and contextually coherent yet inaccurate outputs.

Large Language Models (LLMs) can provide answers that sound realistic to almost any question, even if those answers are entirely made up. With a Graph Database, you can anchor an LLM in reality and mitigate the risk of generating false information or unauthorized access to sensitive data. This prevents the model from producing inaccurate responses and ensures a more reliable and secure outcome.

A graph database uses graph structures with nodes, edges, and properties to represent and store data, facilitating efficient querying and analysis of relationships in interconnected datasets, commonly used for applications such as knowledge graphs, fraud detections, supply.

This presentation will show you the benefits of graph databases over regular databases and how to use AI tools to eliminate LLM hallucinations, enforce security, and improve accuracy. We will also discuss why a vector index can provides better, smarter, faster results than a pure vector database.

  • Agenda:
  • (Times are Central Time)
  • 6:00 pm – brief intros
  • 6:05 pm – Presentation by Jennifer Reif
  • 6:45 pm- Q&A
  • 7:00 pm – End
  • Note: You must register with Zoom in addition to registering through Meetup.

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