Prerequisite: None

Sean Martin

CTO

Cambridge Semantics Inc.

As revolutionary as generative AI applications are, they'll only succeed in enterprise environments if organizations address the many concerns for implementing generative AI in core use cases. These deployments must be sensible, viable, and risk-free to protect companies from problems such as ChatGPT's infamous “hallucinations”—when the system generates nonsensical, inaccurate content.

Making foundational models consumable in enterprise environs requires:

• Model constraint
• Data provenance
• Access controls

Accessing generative AI in knowledge graph environments allays these issues, making model outputs accurate, consistent, traceable, secure, and private.