data.world Expands Snowflake Collaboration with New Exclusive Offering to Accelerate Data Trust
Chat with Data Benchmark: Improving the Accuracy of LLM Responses in the Enterprise
Read the first-of-its-kind benchmark on the accuracy of LLMs in the enterprise
data.world received the highest score for innovation and a rating of "exceptional" in 17 of 18 categories.
Understand the broad spectrum of search and how knowledge graphs are enabling data catalog users to explore far beyond data and metadata.
Join data.world’s Juan Sequeda, Principal Scientist & Head of our AI Lab, Dean Allemang, Principal Solutions Architect, and Bryon Jacob, CTO & Co-Founder, to see how a Knowledge Graph can improve the accuracy of LLM responses by 3X; and learn how a data catalog built on a Knowledge Graph can increase trust in LLMs with clear explainability and governance.
Come join us in our mission to deliver data for all and data for good!
Join data.world’s Juan Sequeda, Principal Scientist & Head of our AI Lab, Dean Allemang, Principal Solutions Architect, and Bryon Jacob, CTO & Co-Founder, to see how a Knowledge Graph can improve the accuracy of LLM responses by 3X; and learn how a data catalog built on a Knowledge Graph can increase trust in LLMs with clear explainability and governance.
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.
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