Session: Leveraging Knowledge Graphs for RAG: A Smarter Approach to Contextual AI Applications
In the ever-evolving field of AI, retrieval-augmented generation (RAG) systems have become critical for delivering high-quality, contextually relevant answers in applications powered by large language models (LLMs). While vector databases have traditionally dominated RAG applications, graph databases, specifically knowledge graphs, offer a transformative approach to contextual AI that’s often overlooked. This approach provides unique advantages for applications requiring deep insights, intelligent search, and reasoning over both structured and unstructured sources, making it ideal for complex business scenarios.
Attendees will leave with an understanding of how to build a RAG system using a graph database and practical skills for data querying and insights retrieval. By comparing graph and vector database approaches, we’ll highlight when and why graph databases may offer superior benefits for managing complex data relationships. The session will provide concrete examples and advanced techniques, empowering participants to incorporate knowledge graphs into their AI systems for better data-driven outcomes and improved LLM performance. This discussion will conclude with a live demo showcasing key techniques and insights covered in this talk.
