Although retrieval augmented generation is a very popular architectural pattern for delivering Q&A over complex internal information, it is also extremely hard to build a high quality, high accuracy RAG system. Come to this session to learn the 7 gotchas of RAG systems and best practices to improve their performance.

There is no value from AI without a Data Strategy. AI hallucinations are a significant risk in delivering ROI across the enterprise. Stardog’s knowledge graph-powered agentic architecture delivers an AI-ready data foundation with a semantic layer that provides facts and grounding needed to eliminate hallucinations. Learn why traditional Retrieval-Augmented Generation and straight Text-to-SQL approaches can be insufficient and how you can broaden AI's access to diverse and dense data and ensure timely, secure, and, most importantly, hallucination-free answers from your own data.

Despite the popularity of retrieval-augmented generation, organizations are struggling to optimize large language model applications based on RAG. Attend this session to get suggestions and recommendations on improving your RAG solution, LLMOps for RAG systems and scaling considerations.

Enterprise-grade GenAI needs a unified data strategy for accurate, reliable results. Learn how knowledge graphs make structured and unstructured data AI-ready while enabling governance and transparency. See how GraphRAG (retrieval-augmented generation with knowledge graphs) drives real success: a major gaming company achieved 10x faster insights, while Data2 cut workloads by 50%. Discover how knowledge graphs and GraphRAG create a foundation for trustworthy agentic AI systems across retail, healthcare, finance, and more.