Generative AI Technologies and Models
Niveau
Second cycle of study, master’s
Learning outcomes of the courses/module
The participants:
• can explain different classes of large language models and compare them based on their application scenarios
• can select relevant models for given use cases
• can identify different technologies for operating LLMs and compare their advantages and disadvantages
• can explain different classes of large language models and compare them based on their application scenarios
• can select relevant models for given use cases
• can identify different technologies for operating LLMs and compare their advantages and disadvantages
Prerequisites for the course
none
Course content
- Different models of LLMs
- Advantages and disadvantages of these models
- Use cases for these models
- Different technology stacks for operating such models
- Challenges in planning and operating local models
- Advantages and disadvantages of these models
- Use cases for these models
- Different technology stacks for operating such models
- Challenges in planning and operating local models
Recommended specialist literature
- Laskhamanan, V.; Hapke, H.: Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications, O'Reilly, 2025
- Gollnick, B.: Generative KI mit Python: KI im Unternehmenskontext – GenAI, Agenten und mehr! Der Guide für RAG-Anwendungen und Agentensysteme mit Vektordatenbanken und LLMs, Rheinwerk, 2025
- Gollnick, B.: Generative KI mit Python: KI im Unternehmenskontext – GenAI, Agenten und mehr! Der Guide für RAG-Anwendungen und Agentensysteme mit Vektordatenbanken und LLMs, Rheinwerk, 2025
Assessment methods and criteria
Portfolio exam
Language
English
Number of ECTS credits awarded
6
Semester hours per week
Planned teaching and learning method
Lecture, group work, presentation, and discussion of assignments
Semester/trimester in which the course/module is offered
2