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Unit of study_
ELEC5623: Applied Generative AI in Engineering
2026 unit information
Generative AI has demonstrated a new landscape and many opportunities for AI engineers to build enhanced and powerful application in a short period of time. This course provides a comprehensive introduction to generative AI techniques, covering the fundamental knowledge, technologies, principles and practices to understand and leverage generative AI for engineering topics. This course is designed for a thorough grounding on the fundamentals and cutting-edge developments of generative AI, to prepare the students for further research or applied endeavours in this new AI engineering era. The students will explore the power of large language models and fine-tuning techniques to craft solutions for engineering tasks.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Postgraduate |
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| Academic unit | School of Electrical and Computer Engineering |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Students must have a basic understanding of artificial intelligence, software engineering and be proficient in Linear algebra, Python Programming |
At the completion of this unit, you should be able to:
- LO1. Understand how to apply generative AI techniques to a variety of downstream applications, and how decisions made during the engineering process affect suitability and performance for these tasks.
- LO2. Demonstrate understanding of the key principles and best practices for systemically engineer downstream applications, such as using prompts for latest LLM models.
- LO3. Describe the key architecture and lifecycle of generative AI systems, and explain how core components enable the generalization to a variety of software engineering use cases, such as the foundation models, prompts and external tools.
- LO4. Evaluate generative AI performance in software systems using standard benchmarks and alignment techniques, and diagnose issues such as bias, drift, hallucination, and prompt sensitivity.
- LO5. Engage in teamwork, drawing on the knowledge, skills and creativeness of all members to deliver a GenAI solution to a particular engineering problem through technical reports, demos, and design documentation that meet engineering standards.
- LO6. Develop critical thinking and problem-solving skills through case studies and project work for the new AI engineering era
- LO7. Identify ethical, legal, and trust-related concerns in generative AI systems, and apply mitigation strategies such as data auditing, prompt filtering, and user-aligned model alignment techniques.
Unit availability
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