Despite the potential, Large Language Models (LLMs) have critical limitations that organizations must overcome to ensure trust. Without trust, it’s nearly impossible to create meaningful business value. In this report, see how a data catalog built on a Knowledge Graph can overcome these limitations by creating a foundation made of AI-ready data. With the organization’s data and knowledge governed and in this flexible format, AI-powered applications have the rich context needed to generate accurate, explainable, governed responses.
In this report, find how how a data catalog built on a Knowledge Graph enable:
Increased accuracy: By sharing enterprise context (rather than relying on statistical methods), a data catalog built on a knowledge graph boosts the relevancy and correctness of LLM responses.
Clear explainability: With that interlinked, flexible, and open graph format in place, now it’s possible to directly trace the LLM responses to enterprise knowledge. Where LLMs were a black box, now they can literally show their work.
Governed responses: With proper governance in place through the data catalog, now organizations can limit what LLMs can access — keeping confidential and proprietary information from being exposed.
Download a complimentary copy of the report to learn how to build the foundation for scalable AI.