Unleashing the Power of Generative AI: Integrating Large Language Models with TigerGraph

Generative AI and Large Language Models (LLMs) are currently experiencing their peak of excitement and attention, offering valuable tools for extracting general information from the vast expanse of the internet. Yet, a pivotal question remains for businesses: how can these models be effectively used to gain insights from their specific data? What if the inquiry is about John Doe's unique financial prospects based on his account status? Or, how can a doctor consult a digital assistant for tailored care solutions considering Maria's medical history?

 

The challenge is twofold: LLMs must comprehend sensitive data without compromising security, and they require accurate foundational facts to provide reliable answers. The integration of LLMs and TigerGraph bridges these challenges, leveraging LLMs' reasoning prowess alongside TigerGraph's comprehensive, up-to-date data representation. This synergistic approach empowers LLMs to tackle intricate queries, such as identifying influential research papers or uncovering potential anomalies in a financial network. By aligning these technologies, business analysts gain a powerful tool to enhance productivity and access richer insights.

Watch this webinar to learn:

• How limitations of using Large Language Models by themselves
• Why LLMs and TigerGraph complement each other
• How to build a natural language interface to TigerGraph using the LangChain library

Speaker - Parker Erickson, Machine Learning Engineer