Embark on a hands-on journey to build your very own Retrieval-Augmented Generation (LLM-RAG) system from the ground up. This workshop is designed to demystify the integration of large language models with retrieval pipelines, enabling you to transform vast data repositories into dynamic, context-aware insights.

During this intensive session, you will:

Understand the Fundamentals: Learn how retrieval and generation work in tandem to produce precise, up-to-date responses.
Build Your Own System: Follow step-by-step guidance to construct an LLM-RAG pipeline—from setting up a retrieval database and generating document embeddings to integrating these components with a self-hosted LLM.
Engage in Practical Exercises: Apply hands-on exercises to fine-tune retrieval strategies and customize your generative model for your unique organizational needs.
Explore Real-World Applications: Discover how LLM-RAG can revolutionize sectors like customer support, legal research, and enterprise knowledge management.
Ensure Data Security: Delve into best practices for maintaining data privacy and secure deployment throughout your AI system.

Ornella Vaccarelli

Ornella Vaccarelli is a Senior Research Scientist at iCoSys and the Lead Scientist at SCAI (Swiss Center for Augmented Intelligence), where she pioneers innovative AI solutions across diverse domains. With expertise ranging from computer vision and computational physics to the latest developments in large language models (LLMs), she bridges cutting-edge research and practical application.

Her collaborative projects span from fundamental research at EPFL—focused on sustainable materials for solar cells—to developing an LLM-RAG system for the Swiss parliament and the Parliamentary Library. Ornella’s work not only advances scientific understanding but also transforms how industry and government leverage AI for informed decision-making.

A prolific researcher, her findings have been published in high-impact journals, and she is a regular speaker at international conferences. Ornella earned her PhD in Computational Physics from Sorbonne University in Paris and holds a Master’s in Theoretical Physics from the University of Bari. Her career exemplifies a commitment to pushing the boundaries of AI while ensuring its responsible and effective integration into real-world applications.

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