The world is rushing to embrace Generative AI and Large Language Models (LLMs), seeing how these tools could drive a massive productivity boost in the workplace. However, to unlock this potential, your enterprise must ensure that your LLMs are grounded in accuracy and explainability. If you don’t, your results could be biased or riddled with hard-to-uncover hallucinations.
To learn how your organization can confidently adopt Generative AI, join this 45-minute fireside chat with Jesús Barrasa, Neo4j’s Head of Solutions Architecture; Nick Johnson, Neo4j’s Senior Product Marketing Manager; along with our special guests, Dr. Ali Arsanjani, Google Cloud; and Geraldene Munsamy, Basecamp Research.
- The distinction between Generative AI and LLMs
- The opportunities and challenges enterprises face in their LLM adoption
- How a Neo4j knowledge graph can help your organization adopt Generative AI with confidence
- The difference between grounding an LLM with a vector database and a knowledge graph
Nick Johnson is a Senior Product Marketing Manager for Graph Data Science at Neo4j, where he works to bring Graph Data Science to life with highly targeted messaging, positioning, case studies, content, and more. Before Neo4j, Nick worked in Product Marketing across several organizations including Microsoft, SAS, and Orbit. Nick holds a Master’s degree in International Development from Sciences Po and Bachelor degrees in International Studies and Swedish from the University of Washington.
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