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Dev Conference by Neo4j
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Session Track: Data Science
Session Time:
Session description
This session revisits a food recommendation tool that helps people make healthier food choices. In the session, you will learn how to upgrade a system that generates personalized recommendations based on Cypher queries to an AI-supported recommendation engine that incorporates a Large Language Model (LLM). The session focuses on comparing two approaches to building AI-supported recommendation engines. The baseline approach uses hard-coded Cypher queries. The additional approaches include using LLM-generated Cypher queries based on a prompt, and an LLM using Retrieval-Augmented Generation (RAG). Both approaches rely on the same eating events graph to serve the recommendations.
Principal Data Scientist, CoreHarmonic
Dr. Alicia Powers is the Principal Data Scientist at CoreHarmonic. Her work focuses on using analytics to help people and businesses make better decisions. She is now creating tools that use Large Language Model to harness the power of the latest Artificial Intelligence technology.