Understanding and Using AI
The training course begins on 10 November, with a technical preliminary course on the tools used. It will quickly become clear that this is not about abstract visions of the future, but about practical, usable know-how. From day one, the course will build on how artificial intelligence – and artificial neural networks in particular – work in a structured way and where their limits lie.

Focus On Materials Science: AI Meets Materials
The second day focuses on materials. Participants gain insights into the current state of AI applications in materials science and engineering. Where can AI already be used today – and what is realistically feasible? The training course provides scientifically sound, application-oriented perspectives on these questions.

Language Models as a Tool: Understanding and Using LLMs
Wednesday marks the start of the core section on working with large language models. Participants will learn how LLMs filter information from large amounts of text, structure content, and recognize patterns. A key topic is prompt engineering, because only those who know how to formulate queries to AI can use it effectively. This will be tried out directly in accompanying exercises.

APIs, Frameworks, and Agents: Infrastructure for Productive Use
The training goes beyond pure application and shows how LLMs can be integrated into existing system landscapes – from cloud variants to self-hosting. Special attention is paid to combining external knowledge sources with LLMs, for example through Retrieval Augmented Generation (RAG). Agents and multi-agent systems are also covered, including tools and concrete application examples.

Trust in AI: Correctness, Explainability, and Responsibility
On the last day, critical reflection takes center stage. What does it mean when AI draws the wrong conclusions? How can results be made comprehensible? And what technical and legal frameworks are relevant here? In a final exercise, material data is interactively analyzed using semantic search – in line with the motto: “Chat with your own data.”

Conclusion: AI Expertise as a Strategic Advantage
Anyone who wants to design data-intensive processes needs a deep understanding of the tools – and their limitations. The training course provides exactly that: solid basic knowledge, technical implementation expertise, and practical application. This enables participants to tap into new potential in their data and launch their own AI projects in a targeted manner. Register today!