Outline

Overview

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

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Students must have a basic understanding of artificial intelligence, software engineering and be proficient in Linear algebra, Python Programming
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Huaming Chen, huaming.chen@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam See Canvas for more details.	40%	Formal exam period	1 hour	AI prohibited
Outcomes assessed:
Interactive oral	Individual task See Canvas for more details.	10%	Multiple weeks	n/a	AI prohibited
Outcomes assessed:
Interactive oral	Individual task Early feedback task. See Canvas for more details.	0%	Week 03	n/a	AI prohibited
Outcomes assessed:
Creative work	Business proposal See Canvas for more details.	10%	Week 06	n/a	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Mid-term test See Canvas for more details.	10%	Week 07	n/a	AI prohibited
Outcomes assessed:
Q&A following presentation, submission or placement	Project presentation See Canvas for more details.	10%	Week 13	n/a	AI prohibited
Outcomes assessed:
Creative work	Project development See Canvas for more details.	20%	Week 13	n/a	AI allowed
Outcomes assessed:
= group assignment ? = early feedback task ?

Assessment summary

Interactive oral: This assessment will cover all material in the unit from both lectures and practical classes. This assessment will require you to explain, justify and reflect on your submitted work in response to questions from the assessor.
Creative work: Working in a group, you will design and develop a project that applies the concepts and techniques covered in the unit to address a practical problem.
Final exam: The exam will cover all material in the unit from both lectures and practical classes. The exam will have a mixture of multiple choice questions and short answer questions.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Why GenAI? Intro and Modern Engineering Applications	Lecture (1 hr)
Week 01	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 02	How GenAI works? Architecture of Foundation Models and API Usage	Lecture (1 hr)
	GenAI environment and model exploration	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 03	Fine-tuning and model customization	Lecture (1 hr)
	Practical fine-tuning task	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 04	Prompt engineering for system control	Lecture (1 hr)
	Prompt engineering and structured outputs	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 05	Advanced Prompt Strategies and Tool Use	Lecture (1 hr)
	GenAI techniques on practical engineering tasks	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 06	Agent Engineering and Autonomous Workflows	Lecture (1 hr)
	Agentic workflow design	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 07	LLM Tools and Deployment	Lecture (1 hr)
	Tool use and function calling	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 08	Red Teaming, LLM Safety and Evaluation	Lecture (1 hr)
	AI system development	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 09	Advanced LLM Architectures and Interpretability	Lecture (1 hr)
	Benchmark and evaluation	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 10	AI Governance and Responsible Deployment	Lecture (1 hr)
	Deployment and LLMOps	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 11	Real-world use cases - I	Lecture (1 hr)
	Security and Red-Team Evaluation	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 12	Real-world use cases - II	Lecture (1 hr)
	Responsible AI and Project Development	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)
Week 13	Course review	Lecture (1 hr)
	Project Demonstration and Evaluation	Practical (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Self-directed learning (6 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Introduction to Generative AI, O'Reilly, 9781638354345
Prompt engineering for generative AI : future-proof inputs for reliable AI outputs at scale, O'Reilly, 1-0981-5339-1
AI engineering : building applications with foundation models, O'Reilly, 9781098166267
Extensive readings and research papers on advanced topics in generative AI and its engineering application topics

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

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.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC5623: Applied Generative AI in Engineering

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links