Relational AI Meets RAG: Enabling Next-Gen LLM Accuracy with Kumo & NVIDIA скачать в хорошем качестве

Relational AI Meets RAG: Enabling Next-Gen LLM Accuracy with Kumo & NVIDIA

LLMs on their own often struggle to provide accurate, contextual answers when working with structured or private data. This webinar explores how combining graph-based predictions with retrieval-augmented generation (RAG) unlocks a new level of performance and reliability for enterprise AI applications. Join us to learn how Kumo’s predictive AI platform and the popular open-source library PyTorch Geometric (PyG) to bring deeper reasoning via retrieving knowledge from graph-structured models. This approach allows LLMs to go beyond static retrieval—incorporating rich, forward-looking context that enables more relevant, precise, and explainable outputs. We’ll walk through real-world examples, share technical insights, and demonstrate how to implement this system using NVIDIA’s containers and Kumo’s prediction capabilities. You’ll leave with a deeper understanding of how to use Relational AI powered RAG to improve your LLM applications and how this architecture can be adapted to your data, domain, and deployment needs. Join us for an in-depth session with Ben Berger, Data Scientist at Kumo, and Rishi Puri, Senior Deep Learning Engineer for PyTorch Geometric at NVIDIA.