Deepen your expertise in Retrieval-Augmented Generation by building, testing and comparing real-world RAG systems across an intensive workshop day

Training content

An accessible introduction to Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Learn how modern RAG systems work and understand their core components, limitations and design choices.

This training is part of a three-day RAG course series. Day 1 focuses on conceptual foundations and can be attended independently, while Days 2 and 3 form a consecutive, hands-on technical module. Participants may register for Day 1 only, for the technical Days 2–3 bundle, or for the complete three-day program. Participants registered for the full 3-day program receive a €50 discount. To register for the complete program, add-on days 2 and 3 in the checkout process.

This training will be conducted in English.

Program

Morning – LLM & RAG Fundamentals

– How LLMs work (tokens, prediction, hallucinations)

– Context window limitations and temperature

– Local vs API-based models (cost, privacy, performance)

– Fine-tuning vs RAG

– RAG principles and architectures

Afternoon – Core RAG Building Blocks

– Document processing (PDFs, text extraction)

– Chunking strategies and metadata

– Embeddings (local vs API)

– Vector storage and retrieval

– Dense vs sparse search (FAISS, BM25)

– Prompt structure and context management

Target audience

– IT services

– Public administrations

– Policy, innovation and digital teams

– Anyone working with or supervising AI solutions

Trainers

Alexandra SÉBASTIEN

Trained in biology and ecology, Alexandra SÉBASTIEN initially focused on social insects (ants and honey bees), their behaviour, bacteria and viruses, and later their proteins. She completed her Bachelor’s and Master’s degrees at Université Pierre et Marie Curie before obtaining a PhD from Victoria University of Wellington in 2016. She subsequently carried out postdoctoral research at the University of British Columbia, where she continued to integrate empirical research with quantitative analysis.

Over time, her work evolved from field and laboratory investigations toward data-driven approaches. This transition led her to undertake a complementary Master’s degree in Big Data at Université Libre de Bruxelles, completed in 2025. She is motivated by analytical problem-solving and by the effort to interpret complex datasets. With the rapid acceptance and use of artificial intelligence in everyday life, she is particularly interested in the societal implications of these tools and in the importance of deploying them in a responsible and constructive manner.

Lyan ALJENDI

Lyan Aljendi is a generative AI engineer at ULB currently focused on building impactful solutions in education. She previously worked on applying generative AI in medical research during her master’s thesis, and she is especially interested in using AI to create positive change in the public sector. She earned a bachelor’s in Software and Information Systems Engineering from Homs University and completed a master’s in Computer Science at ULB in 2025, where she received the Babbage Prize for academic excellence.