The program at a glance
This practice-driven bootcamp offers a unique opportunity to immerse yourself in the world of modern language technologies – gaining real-world experience every single day. You’ll learn how today’s AI is reshaping the workplace and how to strategically apply it in your professional context: from automation and decision support to the development of intelligent systems.
Over eight intensive days, we guide you from a solid methodological foundation to the latest innovations in NLP and LLMs. The focus is on practical applicability, real-world transfer, and forward-thinking concepts.
Bootcamp Content
Foundations & Core NLP Technologies
We begin with a solid introduction to essential NLP techniques such as tokenization and vector representations. You’ll explore how probabilistic models underpin text processing and analysis. Through hands-on exercises, you'll apply these concepts to realistic scenarios and prepare for working with state-of-the-art language models.
LLMs & Agentic AI
The second part of the bootcamp focuses on transformer-based language models like GPT. You'll master the fundamentals of prompt engineering and retrieval-augmented generation (RAG) to optimize model context and begin building advanced LLM applications.
A key highlight is the development of Agentic AI systems – LLM Agents capable of independently structuring tasks, using tools, and making decisions. You’ll learn how to create productive, adaptive, and interactive AI solutions for real-world challenges in business, research, or public services.
You'll also dive into advanced topics such as agent orchestration and multi-agent workflows: How do multiple Agents coordinate effectively? How can you design robust agentic flows for complex business processes? From sequential task handling to parallel agent systems, you’ll gain a deep understanding of orchestrating intelligent workflows.
Model Context Protocol (MCP)
You’ll get hands-on with the Model Context Protocol – an open standard that enables LLMs to access external tools, data sources, and APIs in a consistent and efficient manner.
MCP ensures LLMs are reliably supplied with relevant context information – regardless of the source.
Security, Fairness & Red Teaming
Modern NLP systems must be not only powerful but also secure, robust, and trustworthy. We’ll address key topics like bias detection, fairness, transparency, and MLOps.
Through practical exercises, you’ll explore red teaming techniques to identify vulnerabilities, unwanted outputs, or security risks in LLMs. You’ll learn how to implement effective guardrails to keep your models aligned and safe.
This Bootcamp takes you from theory to practice in just eight days. With a strong focus on future-oriented topics like Agentic AI, MCP, and responsible AI development, you’ll leave this bootcamp ready to shape the future of language AI.
