— with Mathis Börner (Senior Data Scientist, SAP)
When: March 7, 2025, 9am – 3pm
Where: WI Flexroom (A1_04); hybrid
Level: Beginner/Intermediate/Advanced
Category: Career development, data collection, data analysis
Seats: 20 (in-person); no limit (online)
Abstract: Retrieval-Augmented Generation (RAG) – our lifeguard and savior for hallucination in Large Language Models? Beginning with fundamental concepts of LLMs and in-context learning, we’ll address the “Needle in the Haystack Problem” and compare ultra-long context models with RAG approaches. Through practical demonstrations, participants will gain hands-on experience with RAG’s core functionalities and understand its objectives. The session delves into scaling solutions using vector databases and advanced implementations, including chunking strategies, hybrid RAG, and graph-based RAG architectures. We conclude with an overview of emerging trends, examining agentic RAG and the integration of reasoning models in deep research applications. This comprehensive exploration equips attendees with both theoretical knowledge and practical insights into the latest developments in AI language models.
To register for the workshop, kindly fill out this form.
Dr. Mathis Börner serves as Senior Data Scientist at SAP’s Deep Learning Centre of Excellence. After obtaining his PhD in Physics from TU Dortmund, his work now focuses on Large Language Models and Agentic AI.
The workshop is co-organized with Jan Batzner from the WI research group “Digital Economy, Internet, Ecosystems and Internet Policy” and Ramona Picenoni from the WI Career Development.