Advanced LLM Applications and Agentic Systems - Training at EPFL


Johan Berdat joined the SDSC in May 2019 as a Senior Machine Learning Engineer in the Innovation team, based in Lausanne.
After completing his M.Sc. in Computer Science at EPFL, he worked for a few years as a consultant in the industry. Specialized in natural language processing (NLP) and computer vision, he loves challenges in document analysis and knowledge extraction.


Clément Lefeebvre joined the SDSC in January 2019 as a Data Scientist with an emphasis on industry-oriented projects in the Innovation team, based in Lausanne.
He holds a Bachelor of Science degree in Physics, acquired in 2016 from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Following this, he earned a Master of Science in Computational Science and Engineering in 2018, also from EPFL. Throughout his academic journey, Clément concentrated on leveraging Data Science and Machine Learning techniques to enhance efficiency in industrial processes. Subsequently, he developed a specialized interest in Generative AI, with a particular focus on Natural Language Processing (NLP), especially in the application of Large Language Models for innovation.


Klemen Voncina joined the SDSC in April 2026 as a Machine Learning Engineer in the Innovation team, based in Lausanne.
Klemen holds an MSc in Artificial Intelligence from the University of Groningen. Prior to this, Klemen worked for several years in both consulting roles and startups. He brings a strong engineering-focused perspective, combined with a solid background in machine learning. He particularly enjoys tackling practical challenges, especially those related to infrastructure and application deployment. Outside of work, Klemen enjoys a range of outdoor activities such as climbing and mountaineering, as well as more home-based hobbies like board games, reading, and tea.


Kyle (Hogir) van de Langemheen joined the SDSC in September 2025 as a Machine Learning Engineer in the Innovation team, based in Lausanne.
Kyle holds an MSc in Artificial Intelligence from the University of Groningen. He brings several years of experience applying AI across research and industry.
Outside of his professional interests, Kyle enjoys hiking, reading, cooking, and coffee.


Thibaut holds a B.Sc in Computer Science from HEIG-VD. Before joining the SDSC, he worked in startups where he developped a diverse skill set combining cloud infrastructure, database and application development. Thibaut is very enthusiatic about new technologies and best coding practices and he is looking forward to supporting the team and its projects.


Ivan-Daniel joined the SDSC Innovation team in September 2022, where he works as a Data Scientist. He obtained an MSc in Robotics (2022) from EPFL and holds a BSc in Microengineering (2019), also from EPFL. His main fields of interest are Machine Learning, Computer Vision, and animal locomotion modeling.

Presentation
Large Language Models (LLMs) are increasingly used in real-world applications but designing reliable and scalable systems remains challenging. This 1-day workshop focuses on the practical development of LLM-based applications using Python. Participants explore key design patterns such as structured outputs, retrieval-augmented generation (RAG), tool integration, and agentic workflows, while also addressing engineering considerations such as evaluation, robustness, and cost control.
Main objectives
- Understand the architectural components of modern LLM-based systems (structured output, APIs, context management, tool integration, …)
- Implement structured outputs and simple agentic mechanisms in a real use case
- Evaluate LLM system performance using appropriate metrics and testing strategies
- Identify and mitigate common failure modes (hallucinations, prompt brittleness, cost inefficiencies)
- Acquire practical guidelines for moving from prototype to production-ready systems
Topics covered
LLMs, history, terminology, and concepts / Structured outputs and tool calling – enabling LLMs to interact with external systems, extract structured data, and follow precise formats / Advanced techniques & Agent design patterns – RAG, reasoning loops, multi-agent systems, routing strategies workflows / Production considerations – evaluation frameworks, cost management, debugging strategies, and fail-safe mechanisms.
Details
Prerequisites
To make the most of this course, participants should have:
• Intermediate programming skills in Python
• Basic understanding of LLM applications
Participants are required to bring their own laptop for hands-on exercises and have to make sure that they can use Renku.
Course fee*
1,000 Swiss Francs
* 10% special discount for contributing members of EPFL Alumni and EPFL VPI partners, SDSC partners, EPFL AI Center and SNAI partners.
Programme
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