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.
Jennifer Reif is a Developer Advocate at Neo4j, speaker, and blogger with an MS in Computer Management and Information Systems. An avid developer and problem-solver, she has worked with many businesses and projects to organize and make sense of widespread data assets and leverage them for maximum business value.
Jennifer has expertise in a variety of commercial and open source tools, and she enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.
(Chicago Central time)
6:00 pm - Brief introductions
6:05 pm - Presentation by Jennifer Reif
6:45 pm - Q&A
7:00 pm - End
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