Syllabus
1. AI Basics
- Become familiar with the most important types of AI technologies and know where they can be used effectively in requirements engineering.
- Understand what happens "behind the scenes" when interacting with a chatbot - including the differences between retrieval augmented generation (RAG) and fine-tuning.
2. Large Language Models (LLMs)
- Understand how large language models actually work - that they predict text instead of really "understanding" it.
- Recognize typical strengths, limitations and risks of LLMs and assess when their use in RE makes sense.
3. Prompt Engineering
- Realize how crucial a clear, complete context is to get good results from AI systems.
- Know proven prompting patterns and techniques (e.g. role-task format, chain-of-thought, zero/few-shot) and use them specifically to achieve better results.
4 Risks and Responsibilities
- Know the most common risks of AI in requirements engineering - such as hallucinations, bias or data protection problems - and can actively manage them.
- Understand your role as a requirements engineer in an AI-supported process and take responsibility for the verifiable and trustworthy use of AI results.
5. Application Scenarios in Requirements Engineering
- Recognize how AI supports you in all phases of requirements engineering - from elicitation and documentation to validation and management.
- Know suitable tools and methods to make your RE activities more efficient and consistent.
6 AI Terminology
- Know the key terms and concepts in the interaction between AI and requirements engineering - from embeddings and context windows to RAG and fine-tuning.
- Use these technical terms confidently and understand their meaning in RE projects and in the AI4RE context.