Abstract
This talk focuses on GraphRAG, an advanced Retrieval-Augmented Generation method that represents knowledge as interconnected nodes. We'll get into its architecture, implementation challenges, and performance gains in multi-hop reasoning tasks. Learn how GraphRAG is transforming knowledge management for large language models, improving accuracy and coherence in complex inference scenarios.
The talk is ideal for AI engineers, ML researchers, and developers working on knowledge-intensive NLP tasks, chatbots, question-answering systems, or any application requiring complex reasoning and factual accuracy from LLMs.
Key takeaways:
- Understanding GraphRAG architecture and its advantages over traditional RAG
- Techniques for constructing and querying knowledge graphs for LLM context
- Best practices for integrating GraphRAG with existing LLM pipelines
- Performance metrics and case studies demonstrating accuracy improvements
- Challenges and future directions in graph-based knowledge retrieval for AI
About the Speaker
Guy Korland serves as CEO at FalkorDB, where he drives graph database architecture for generative AI and retrieval-augmented generation workflows. He holds a PhD in Computer Science from Tel Aviv University and brings over 20 years of experience in database engineering. He previously led Redis’ incubation arm as SVP & CTO, oversaw platform architecture as GM & CTO at Stor.ai (Self-Point), co-founded and served as CTO of Shopetti, and directed R&D as VP at GigaSpaces.
