Grounded Intelligence: Boosting LLMs with Real-Time Graph Context

What if your language model could tap into live, interconnected facts at scale? This session introduces graph databases and using graph as context for AI applications, an approach that merges dynamic graph queries with advanced language models to deliver precise, context-aware responses. We’ll demonstrate how domain-specific constraints, real-time data ingestion, and semantic relationships are used to keep generation grounded in verified information. You’ll learn about key architectural considerations—such as optimizing graph indexing, enforcing consistency rules, and ensuring transparent traceability—to maintain both accuracy and clarity in AI workflows. By the end, we’ll show how graph elevates AI from a black box novelty to a reliable asset in applications.