It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology.
Graphs and graph data science are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources.
In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.