A two-part course on integrating AI/LLM tools into research workflows, with emphasis on good software engineering practices.
This course teaches Master students (non-CS disciplines) how to effectively use Large Language Models (LLMs) in their research projects while following best practices for reproducibility and code organization.
| Part | Topic | Duration | Code Example |
|---|---|---|---|
| 1 | Good Development Practices for Research Projects | 90 min | name_parser |
| 2 | Local LLMs with Ollama & Model Context Protocol (MCP) | 90 min | chat_with_pdf |
Sebastian Zug, Professor for Software Development and Robotics at TU Bergakademie Freiberg
- Email: sebastian.zug@informatik.tu-freiberg.de
- Institution: TU Bergakademie Freiberg, Institute of Computer Science
- Understand why good project structure matters for reproducibility
- Manage dependencies and configurations safely
- Use Git for version control
- Integrate LLM APIs into Python projects
- Run local LLMs with Ollama (Part 2)
- What are your experiences with AI/LLM tools in research?
- Which programming languages and tools do you use?
- What challenges have you faced in managing research code?
MIT License