What if your language model could tap into live, interconnected facts at scale? This session introduces GraphRAG, an approach that merges dynamic graph queries with advanced language models to generate precise, context-aware responses while minimizing hallucinations.

Key Discussion Points

• Enhancing AI with graph-driven context by integrating real-time data ingestion and semantic relationships to improve response accuracy.
• Grounding AI in verified knowledge using domain-specific constraints and structured data to ensure fact-based and relevant responses.
• Architectural considerations for GraphRAG, including optimizing graph indexing for retrieval speed, enforcing consistency rules to maintain data integrity, and ensuring transparent traceability for better explainability.
• Turning AI into a reliable asset by demonstrating how GraphRAG transforms LLMs from experimental tools into enterprise-ready knowledge assistants.

This session provides practical insights and implementation strategies for teams looking to anchor AI outputs in structured, real-time data, enabling trustworthy AI applications.

Target Audience

This session is ideal for AI engineers, data scientists, enterprise architects, and knowledge graph experts interested in grounding AI models with real-time data. It’s also valuable for CTOs, AI product leaders, and innovation teams seeking scalable, explainable AI architectures for enterprise applications.