AI4RE – Artificial Intelligence in Requirements Engineering
Artificial intelligence – particularly Large Language Models (LLMs) – is changing the way requirements are collected, documented, validated and managed. But where does AI actually benefit in requirements engineering (RE), what are its limitations and what risks must be taken into account? In this practice-oriented day course you will learn how AI can be used responsibly and purposefully in requirements engineering. You will develop a sound understanding of modern AI concepts, learn how LLMs work and the special features of LLMs, and learn how AI can meaningfully support RE activities throughout the entire life cycle. The course combines theoretical fundamentals with concrete application examples, best practices for prompt engineering as well as aspects of AI governance and compliance.
Online assessment possible Foundation
Goals
- Explain and classify the basic concepts of modern AI in requirements engineering
- Realistically assess the capabilities, limitations and typical risks of Large Language Models (LLMs).
- formulate targeted prompts and critically examine, validate and improve AI results
- Identify and address risks, responsibilities and regulatory aspects (e.g. hallucinations, bias, data protection, confidentiality, human-in-the-loop)
- Evaluate typical application scenarios of AI in requirements engineering and use them depending on the context
- Preparation for AI4RE Micro Credential from IREB
Target Groups
Content
1. AI basics for requirements engineering
- What is AI – and what isn’t?
- Overview of AI technologies in the RE context (NLP, Vision, ML, Expert Systems)
- Realistic possible uses and limitations of AI in RE
2. Understand Large Language Models (LLMs).
- Basic concepts of LLMs
- Core mechanism: prediction rather than understanding
- Probabilistic generation and its consequences for RE artifacts
3. Prompt engineering in requirements engineering
- Meaning and construction of context
- Use context correctly (roles, goals, quality criteria)
- Prompting patterns and techniques for typical RE tasks
4. Risks and responsibilities
- Typical risks when using LLMs in RE (e.g. hallucination, bias)
- Data protection, confidentiality and compliance
- AI governance and human-in-the-loop
5. Use cases for AI in requirements engineering
- Requirements gathering: exploration, interview guides, transcription
- Documentation: formulation, consistency check, glossaries
- Validation: Detecting contradictions, ambiguities and quality defects
- Requirements Management: Attributes, prioritization and change support
Certification
The training is based on the current IREB AI4RE curriculum and supports you in optionally acquiring an AI4RE micro-credential (digital micro-badge). A voluntary online check is available for this purpose.
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