It’s no secret that Large Language Models (LLMs) are popular, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights but are not without challenges. 

Grab your lunch and join Katie Roberts, Ph.D and Yizhi Yin, Ph.D., two of Neo4j’s top data scientists, on December 7 for a technical session and demo to learn how graphs and graph data science can be incorporated into your analytics practice to help reduce hallucinations, generic responses, bias, and a lack of traceability.

During the lunch and learn, we will cover:

  • How a knowledge graph can improve the explainability and accuracy of LLM applications.
  • The advantages of using connected data for retrieval augmented generation (RAG).
  • How to enable contextual and semantic information retrieval.

We’ll end with an enterprise-ready database demo that will show you how easy it is to get started.

Please feel free to share this event with your colleagues.

Katie Roberts, Ph.D.
Data Science Solution Architect

Katie is a Data Science Solution Architect at Neo4j. She completed her degree in Cognitive Neuroscience at Harvard University. Passionate about people and problem solving, she transitioned to focusing on helping people and businesses leverage data for impactful outcomes. As a customer-facing data scientist, she has had the opportunity to work with large and small organizations across a variety of industries. At Neo4j she helps teams up-level their data science practice with graph data science. 

Yizhi Yin, Ph.D.
Solutions Engineer

Yizhi Yin is a Solutions Engineer at Neo4j. Having earned a Ph.D. in Genetics from the University of Iowa and several years of postdoctoral research in cancer biology, Yizhi transitioned into data science at Tamr, working with large and small organizations to adopt machine learning to enhance data quality. In her current role at Neo4j, Yizhi collaborates closely with clients to start their journey with graph databases and graph data sciences.

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