Srileo LearnProfessional Learning Platform

IREB AI4RE MICRO-CREDENTIAL

AI4RE Micro-Credential Prep

Complete preparation for the AI4RE micro-credential exam — every educational objective, worked examples from production systems, chapter quizzes and three full mock exams. Free, with no enrolment step.

  • 10 modules
  • 24 lessons
  • 12 quizzes
  • 156 questions
  • About 9 hours

Free — no enrolment needed

Start the courseSign in to save progress

Refund and cancellation policyLearner terms

Course syllabus

Nine chapter modules cover all six educational units of the AI4RE syllabus, followed by an exam-preparation module with three full 22-question mock exams at the real 80% pass mark. Every lesson and every quiz is free.

  1. 1. AI Basics

    The five families of AI technology and where each one fits, the six stages a chatbot runs between your prompt and its reply, and how RAG and fine-tuning differ as ways to give a model knowledge it was not trained on.

  2. 2. Large Language Models

    What an LLM actually does — probabilistic next-token prediction rather than reasoning — and what follows from that for a Requirements Engineer: non-determinism, hallucination, the surface and factual layers, and the embeddings that make semantic search possible.

  3. 3. Prompt Engineering

    Why context decides output quality, the difference between the system context you cannot change and the user context you control, and how reusable prompting patterns differ from situational prompting techniques.

  4. 4. Risks in AI4RE

    The ten risks the syllabus names when AI is used in Requirements Engineering, grouped into those that come from the model itself, those that come from data and law, and those that come from how people use the output.

  5. 5. Responsibilities in AI4RE

    How the Requirements Engineer's role broadens when AI enters the process: process designer, prompt designer and quality reviewer, risk and compliance manager, and stakeholder communicator — plus the environmental impact of how AI is used.

  6. 6. AI in Elicitation

    Three areas where AI supports elicitation — exploring an unfamiliar domain, transcribing stakeholder communication, and extracting requirements across formal and informal sources — and the human curation each one still requires.

  7. 7. AI in Documentation

    The two closely connected documentation activities the syllabus distinguishes — formulating requirements in standardised formats, and transforming them across representations and audiences — with what AI contributes to each and what it cannot decide.

  8. 8. AI in Validation and Management

    The inherent limitations of AI-generated requirements and what validating them demands, followed by the two requirements management tasks AI supports: assigning attributes and proposing prioritisations.

  9. 9. AI Terminology

    The examinable glossary: the model and architecture terms that describe how AI systems are built, and the interaction and risk terms that describe how you work with them and where they fail.

  10. 10. Exam Preparation

    How the AI4RE micro-credential exam is structured, how the eleven educational objectives map onto question types, a study plan, and three full-length mock exams at the real 22-question, 80% format.

Source and attribution

Based on the IREB AI4RE micro-credential syllabus and study guide v1.0.0 (1 March 2026) by Gunnar Harde, Matthias Herbert, Simon Jimenez, Daniela Kreiner, Michael Mey, Michael Tesar and Franz Zehentner. © IREB e.V. Used as the basis for training material under the terms of use published with that document. This course is independently written and is not endorsed by or affiliated with IREB e.V.