IREB® CPRE AI4RE micro-credential
The IREB® CPRE AI4RE micro-credential covers practical use of AI in Requirements Engineering. Participants learn how large language models, chatbots, and retrieval-augmented generation can support requirements elicitation, documentation, validation, and management.
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Description
The IREB® CPRE AI4RE micro-credential covers practical use of AI in Requirements Engineering.
Participants learn how large language models, chatbots, and retrieval-augmented generation can support requirements elicitation, documentation, validation, and management. The course addresses prompt formulation, critical review of AI results, and the limits of AI tools where human expertise is required. It also covers risks and responsibilities, including bias, data protection, and confidentiality.
After completion, participants can apply AI methods more responsibly in RE work and prepare for the short online assessment and digital badge.
What You Will Learn
Course content: IREB® CPRE AI4RE micro-credential
This training follows the five chapters of the official IREB AI4RE syllabus and study guide. It uses trainer-led explanation, practical examples, discussion, and short exercises to prepare participants for responsible AI use in Requirements Engineering and the online assessment for the digital badge.
Module 1: AI Basics
Core AI technologies, including machine learning, deep learning, natural language processing, computer vision, robotics, and expert systems.
How chatbots process prompts, context, and outputs.
Retrieval-Augmented Generation, RAG, compared with fine-tuning for adding project knowledge.
Module 2: Large Language Models, LLMs
LLMs as probabilistic text prediction systems.
Key concepts such as tokens, embeddings, temperature, context limits, and hallucinations.
Capabilities and limits of LLMs when used for requirements-related text.
Module 3: Prompt Engineering
Prompt context, system context, and user context in RE tasks.
Prompting patterns such as Role-Task-Format, CRISPE, zero-shot, few-shot, and template-based prompting.
How to review AI outputs critically before using them in project work.
Module 4: Risks and Responsibilities in AI4RE
Risks including bias, confidentiality, data protection, intellectual property, outdated knowledge, model drift, and over-reliance on AI.
Environmental impact of AI use, including energy demand and repeated prompt cycles.
The Requirements Engineer’s role in human control, validation, traceability, explainability, and stakeholder communication.
Module 5: Use Cases for AI in RE
Elicitation support, including domain exploration, stakeholder interview preparation, transcription, and requirements extraction.
Documentation support, including ISO/IEC/IEEE 29148-style requirements, user stories, acceptance criteria, glossary work, and format transformation.
Validation and management support, including consistency checks, test case derivation, attribute assignment, and prioritization suggestions.
Certification & Exam
The IREB® CPRE AI4RE micro-credential confirms focused knowledge of Artificial Intelligence for Requirements Engineering. The assessment has no prerequisites and is completed through a short online, open book exam.
For AI4RE, the exam includes about 22 multiple-choice questions and lasts 30 minutes. Non-native speakers may request an extension to 36 minutes. Candidates must score at least 80% of the total points.
Notes, textbooks, and other resources may be used, but assistance from other people and the use of AI assistants are not allowed. Successful candidates receive written confirmation and a digital badge, valid for three years. T
he content covers LLMs, prompting, RAG, risks, responsibilities, and AI use cases in elicitation, documentation, validation, and requirements management. Reference: official IREB AI4RE micro-credential and examination regulations.
What You Will Achieve
After completing the IREB CPRE AI4RE micro-credential, participants will be able to:
Classify the basic principles of modern AI as they apply to Requirements Engineering.
Describe the capabilities and limitations of large language models (LLMs), chatbots, and retrieval-augmented generation (RAG) in requirements work.
Apply AI tools to support requirements elicitation, documentation, validation, and management while maintaining human review and control.
Formulate targeted prompts for Requirements Engineering tasks and assess AI-generated results for accuracy, relevance, and completeness.
Analyze risks related to bias, data protection, confidentiality, and professional responsibility when using AI in requirements activities.
Evaluate suitable AI use cases in Requirements Engineering and decide where domain expertise and human judgment remain necessary.
These outcomes align with the IREB AI4RE micro-credential focus on practical AI use in Requirements Engineering, including LLMs, chatbots, RAG, prompting, risk awareness, and RE application areas.
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