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
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