Retrieval-Augmented Generation enables language models to answer questions using information retrieved from a defined collection of documents. This workshop offers a practical introduction to how a RAG system works—from document ingestion and retrieval to conversational question answering—and how its results can be evaluated. The workshop uses an open-source stack, giving participants a transparent and self-hostable foundation for their own projects.

What participants will do

Participants will work through the main components of a RAG pipeline and build a document question-answering system. They will explore document ingestion with tools such as Docling, connect retrieved content to an LLM-based chatbot interface, and gain a basic understanding of RAG evaluation. The workshop is designed so that participants can understand and continue using the template after the event.

What to expect

Participants can expect a hands-on technical session with a practical outcome: a RAG template that they can take home and build on. No previous experience with RAG is required, but participants should have basic Python knowledge and bring a laptop with Python 3.10 or later and Docker installed. Before attending, they should complete the setup guide: https://github.com/aihpi/workshop-getting-started/