Andreas Kollegger

Join us at the Neo4j booth! Walk with me along some agentic workflows where LLMs and graphs meet. Graphs are great, but building them can be daunting. This is a marvelous task for a team of agents, specialized in each aspect of knowledge graph construction.

RAG & LLM Frameworks: Learn about the power of GraphRAG (Graph Retrieval Augmented Generation) to improve the accuracy, relevance, and quality of LLM responses. While LLMs offer great potential, they can face challenges with lack of domain knowledge and hallucination.

RAG & LLM Frameworks: This talk will share up-to-date learnings from the evolving field of knowledge graphs: from live case study examples at many of the world’s largest organizations, to the latest technologies & integration patterns, to how to think about knowledge graphs more generally and where they add value.

RAG & LLM Frameworks: Learn about practical graph design patterns and retrieval strategies to more effectively customize GenAI for real-world applications. While GenAI offers great potential, it faces challenges with hallucination and lack of domain knowledge.

The classic RAG application extends an LLM with private information, able to fetch answers to questions that are contained in a single chunk of text. What if the answer requires connecting the dots across multiple chunks that may not be directly similar to the question?