Large Language Models Engineering

Target Audience

The programme is intended for students interested in applied artificial intelligence, natural language processing, and software engineering. It is especially relevant for those who wish to gain practical expertise in building, fine-tuning, and deploying Large Language Models (LLMs), and for students who are motivated to apply these technologies in real-world professional contexts such as healthcare, education, logistics, or creative industries.

Additional Support for students with less prior knowledge or programming experience

To accommodate differences in prior knowledge and programming experience, the minor provides differentiated learning support. During the first two weeks, students complete short diagnostic coding tasks to identify their individual skill levels. Those who need reinforcement receive guided practice materials and mentoring during lab hours, while more advanced students are encouraged to take on extended challenges or contribute to peer support. This ensures that all participants can engage effectively with the technical components of the course regardless of their background.

The work forms are designed to support progressive competency development. Students begin with guided labs and workshops that emphasize technical proficiency, then move to case analyses and ethical debates that require independent judgment and reflection. Finally, in the capstone project, they integrate all competencies, technical, ethical, and communicative, in a self-directed professional context. This gradual increase in autonomy and complexity ensures the continuous growth of Bachelor-level professional ability.

This minor uses a blended approach of lectures, labs, workshops, and project-based learning, designed to meet the intended learning objectives and support Bachelor-level competences.

• Lectures and Seminars provide the theoretical foundation (e.g., NLP principles, transformer architectures, responsible AI frameworks) and are always connected to practical cases (Learning Objective 1).
• Hands-on Labs and Coding Workshops translate theory into practice, allowing students to experiment with models, tools, and pipelines (Learning Objective 2).
• Case Studies and Ethical Debates are used to analyze real-world issues of bias, privacy, and safety (Learning Objective 3 and 4).
• Group Assignments and Peer Learning simulate professional collaboration, strengthen communication and teamwork skills, and provide opportunities for multidisciplinary interaction (Learning Objective 4 and 5).
• Capstone Industry Project enables students to integrate all competences into an authentic professional context. It develops investigative, critical, and reflective capacity, while encouraging self-directed and lifelong learning (Learning Objective 1, 2, 3, 4, and 5).
• Supervision and Feedback Sessions ensure continuous reflection on progress and promote proactive learning strategies (Learning Objective 2, 3, 4, and 5).

Assessment:

• Individual Weekly Assignments – Weekly technical coding tasks providing continuous feedback to strengthen technical understanding and prepare students for summative assessments. Formative, linked to learning objectives (2, 5).
• Capstone Project Proposal – Team submission including project problem, system architecture, and ethical considerations. Provides structured formative feedback before the final project. Formative, linked to learning objectives (2, 3).
• Supervision & Peer Feedback – Continuous participation in supervision meetings and peer-feedback sessions to support reflective learning and collaboration. Formative, linked to learning objective (5).
• Final Exam – Written or online exam assessing theoretical and ethical understanding of NLP, LLM architectures, and responsible AI frameworks. Summative, linked to learning objectives (1, 3). Weight: 20%. Minimum passing grade 65%.
• Ethics Case Study – Analytical written assignment addressing ethical and societal challenges in LLM deployment. Summative, linked to learning objectives (3, 4). Weight: 15%. Minimum passing grade 65%.
• Capstone Project – Comprehensive industry-linked project delivering an end-to-end LLM application with documentation, evaluation, and ethical assessment. Summative, linked to learning objectives (2, 3, 5). Weight: 60%. Minimum passing grade 65%.
• Final Presentation & Defense – Oral defense of the capstone project before instructors and/or industry partners, demonstrating integration, communication, and reflection. Summative, linked to learning objectives (4, 5). Weight: 5%. Minimum passing grade 65%; Mandatory participation.

If you have successfully completed this exchange programme then you are able to:

• Explain the principles of natural language processing, transformer architectures, and large language models. [Knowledge and Understanding]
• Design, implement, and deploy LLM-powered applications using state-of-the-art tools and frameworks (e.g., Hugging Face, LangChain, vector databases). [Applying Knowledge and Understanding]
• Critically evaluate ethical, societal, and technical implications of LLMs, including bias, privacy, and safe deployment, and integrate responsible AI principles into system design. [Making Judgments]
• Collaborate in multidisciplinary teams and effectively communicate technical designs, ethical assessments, and project outcomes to both technical and non-technical stakeholders. [Communication]
• Independently acquire new knowledge in the rapidly evolving field of AI and apply it to innovative professional contexts, demonstrated in the capstone industry project. [Learning Skills]

Fall 2026

The exact weekly schedule for the semester is currently under development, but will follow the methods, assessments and learning goals described above.

After completing your exchange programme at Rotterdam University of Applied Sciences, you will receive a:

• Transcript of records

The programme does not consist of several individual courses with individual credits, but is a holistic unit that combines lectures, labs, workshops and project-based learning.

The 30 EC of this course will be awarded in full at the end of the semester if all learning outcomes are met.

If a retake is necessary, we will try our best to organise this before the end of the semester. If this is not possible before the end of the semester, the teacher will make individual arrangements with the student to do the retake from their home university.