CityUHK · IS5542 · Generative Artificial Intelligence for Business

CityUHK IS5542: Generative Artificial Intelligence for Business — course overview with AskSia

IS5542 Generative Artificial Intelligence for Business is a 3-credit-unit postgraduate course from the Department of Information Systems at City University of Hong Kong. It is one of the three AI-group core courses of the MSc Artificial Intelligence in Business and an elective in the MSc Business and Data Analytics, MSc Digital Transformation and Technological Innovation and MSc AI-Driven Innovation. The 2025/26 catalogue lists it in Semester B; the MSc BDA page lists it for Semester A or B.

3 credit points Postgraduate (P5/P6) Offered Semester B ~50% exams Department of Information Systems

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Sia generates IS5542 practice questions, walks through introduction to generative ai for business and understanding large language models step by step, and quizzes you on the material the exam weights most heavily.

Which thesis is stronger?

Sharpen your argument

Pick one · the reasoning is revealed after you answer

A retailer wants a chatbot that answers order questions from its own policies. The team plans to prompt a general model with no other changes. Which response best applies the course?

Why this one wins

The syllabus moves from conversational design (intents and responses) through training and fine-tuning to integration and ethics.

A general model has no knowledge of the retailer's policies; grounding or fine-tuning supplies it, and evaluation metrics check it.
Integration topics include scalability, security and privacy, which must be designed before deployment, not after.
Blanket rejection ignores the course's premise that generative AI can drive value when designed properly.

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your whole grade↘

Where your grade comes from Exams 50% · Presentations 40% · Quizzes 10%

1040Examination (2 hours) · 50%

One exam decides 50% of your grade. 2 hours; minimum 20%; must be passed. This whole page is built around that.

Overview

What IS5542 is, and where it sits

The syllabus explores the practical applications and implications of generative AI, such as ChatGPT, in business: developing intelligent conversational agents, automating customer interactions, enhancing decision-making and driving value across business domains. Seminars (three hours a week) cover concepts, frameworks and strategies; case studies analyse how large language models are used in business domains; hands-on exercises design, train and evaluate generative models and weigh ethical and legal considerations; a group project applies the models to a selected domain.

Assessment is 10% participation and exercises, 40% group project and a 50% two-hour written examination. Students must pass both the coursework and the examination, with published minimums of 30% and 20%.

What to focus on. The course grades your ability to use the tools it studies: GenAI is explicitly allowed on the exercises and the group project, and the rubric criteria ask whether you can design, train and evaluate a model for a business use, not only describe one. The written paper then checks the concepts without the tools.

Always treat your own course outline and the exam timetable as authoritative.

Difficulty & time commitment

Is IS5542 hard, and how much time does it take?

IS5542 is manageable if you keep a weekly rhythm and treat the back half as the main event. The pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.

Difficulty

3.1 / 5

Moderate. Gentle early, demanding back half. Hard to fail with steady work; a top grade takes consistent practice.

Exam load

50%

The exams decide most of the grade. The heaviest single component is 50%.

Generative AI and large language models: concepts, capabilities, limitssteady

Conversational design; training and fine-tuning modelssteep

Integration, ethics and regulation; industry case studiessteady

Group project; future trends; examinationsteep

The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the course.

Is this course for you

Who tends to do well, and who tends to struggle

You will likely do well if

You want to build and evaluate a language-model application for a business problem, not only discuss one.
You can move between a technical limitation and its business consequence.
You take the ethics and regulation block as seriously as the modelling block; it is assessed.

You may struggle if

You expect a prompt-writing course; fine-tuning, evaluation metrics and deployment are on the syllabus.
You let GenAI write the project without understanding the pipeline; the exam has no tools.
You skip the impromptu quizzes and presentations; they are 10% and set the exam's level.

do this ↘

What top students do differently

For each case study, write the business problem, the model used, the metric that judged it and the risk it raised.
Build the group project as a pipeline you can explain end to end: data, fine-tuning, evaluation, deployment, safeguards.
Revise the six CILOs as six exam-question types.

Syllabus

The 10 topics, topic by topic

Topics from the public source. Their order is a content map, not an official weekly timetable or published exam weighting.

T1 · Introduction to generative AI for business

Keyword syllabus 1; CILO 1

Overview and significance; large language models in business; the ethical and legal frame.

Study topic — official weight not publishedQuiz me on introduction to generative ai for business →

T2 · Understanding large language models

Keyword syllabus 2; CILO 1

Architecture and capabilities; generative models versus chatbot frameworks; sequence models and reinforcement learning; limits in business contexts.

Study topic — official weight not publishedQuiz me on understanding large language models →

T3 · Designing conversational flows

Keyword syllabus 3; CILO 5

Conversational design principles; user intents and system responses; handling complex queries and context.

Study topic — official weight not publishedQuiz me on designing conversational flows →

T4 · Training and fine-tuning large language models

Keyword syllabus 4; CILO 3

Dataset preparation, architecture and training strategy, evaluation metrics, transfer learning and domain fine-tuning.

Study topic — official weight not publishedQuiz me on training →

T5 · Integrating models into business applications

Keyword syllabus 5; CILO 5

Deployment options, integration with customer-service systems, scalability, security and privacy.

Study topic — official weight not publishedQuiz me on integrating models into business applications →

T6 · Ethical and legal considerations

Keyword syllabus 6; CILO 4

Bias, fairness and transparency; intellectual property; data protection; regulatory frameworks.

Study topic — official weight not publishedQuiz me on ethical →

T7 · Case studies and industry applications

Keyword syllabus 7; CILO 2

Marketing, product design, financial forecasting, personalised experience, decision support and knowledge management.

Study topic — official weight not publishedQuiz me on case studies →

T8 · Innovation and future trends

Keyword syllabus 8; CILO 6

Emerging trends; implications for innovation, customer experience and workforce augmentation.

Study topic — official weight not published

T9 · Practical projects and hands-on exercises

Keyword syllabus 9; CILO 3

Implementing models with popular frameworks, solving business problems, presenting outcomes, optimising performance.

Study topic — official weight not publishedQuiz me on practical projects →

T10 · Group project: report and presentation

AT2

Applying generative AI concepts and techniques to a selected business domain.

Study topic — official weight not published

How it's assessed

Assessment structure

Component	Weight	Format & timing
Participation and exercises: impromptu quizzes, small-group discussion, role-plays, self-reflection, student presentations	10%	Impromptu quizzes, paired or small-group discussions, role-plays, self-reflection and student presentations; GenAI allowed. Throughout the semester. Individual.
Group project: report and/or presentation applying generative AI to a selected business domain	40%	Group project with a report and/or presentation applying generative AI to plan, develop or evaluate large language models in a chosen business domain; GenAI allowed. Across the semester. Group; coursework must reach 30%.
Examination (2 hours)	50%	Written examination on the topics taught. Examination period. 2 hours; minimum 20%; must be passed.

Participation and exercises: impromptu quizzes, small-group discussion, role-plays, self-reflection, student presentations10%

Impromptu quizzes, paired or small-group discussions, role-plays, self-reflection and student presentations; GenAI allowed.

Group project: report and/or presentation applying generative AI to a selected business domain40%

Group project with a report and/or presentation applying generative AI to plan, develop or evaluate large language models in a chosen business domain; GenAI allowed.

Examination (2 hours)50%

Written examination on the topics taught.

Participation and exercises 10%, group project 40%, examination 50% (2 hours). Students must pass BOTH coursework and examination; minimum continuous assessment passing requirement 30%, minimum examination passing requirement 20%. GenAI is allowed on the coursework tasks.
A two-hour written examination worth 50%, developed to assess competence in the topics taught, with rubric criteria spanning all six CILOs: describing generative AI concepts and conversational agents, applying large language models in business domains, designing and evaluating models, ethical and legal analysis, automating customer interactions, and evaluating business impact. A minimum of 20% on the examination is required.

read this! If you read nothing else

This is an exam-cram course. With the exams at 50% of the grade and the examination (2 hours) alone at 50%, your result is overwhelmingly decided by how well you perform under time pressure. 2 hours; minimum 20%; must be passed.

Final exam timing: Written examination, 2 hours, in the examination period. Confirm the exact date and venue on your exam timetable.

How to actually pass it

A weekly rhythm, two checklists, and the traps to avoid

The course rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.

The weekly loop

Seminar (3 h)

Note the concept, the framework and the business use.

Same week

Reproduce the hands-on exercise and change one parameter.

Fortnightly

Advance the group project pipeline by one stage.

The mistakes that cost marks

Build a study plan with Sia → Drill the back-half topics →

Formula & concept sheet

The vocabulary and formulas you must own

Generative AI: Models that produce new text, images or other content from learned patterns.
Large language model: A neural network trained on text at scale to generate and represent language.
Fine-tuning: Further training a pre-trained model on domain data for a specific task.
Transfer learning: Reusing a model trained on one task for another.

Conversational agent: A system that interacts with users through dialogue.
Intent: The user's goal that a conversational system must recognise.
Evaluation metric: A measure used to judge a generative model's outputs.
Model bias: Systematic unfairness in a model's outputs arising from its data or design.

Drill these as flashcards → Map them with Sia →

Where it fits

Prerequisites, related courses & why it matters

No prerequisites; 3 credit units, P5/P6. AI-group core of the MSc Artificial Intelligence in Business; elective in the MSc BDA (IAM) and other IS master's programmes.

ExploreAll Information Technology coursesCityUHK discipline hub BibleFull IS5542 exam bibleEvery chapter, worked examples and exam strategy

Why it matters beyond the grade. Product, consulting and analytics roles are asked to deploy language-model applications responsibly; this course pairs the build with the governance.

Your IS5542 study toolkit

Study the course with Sia, not just read about it

Use your permitted course notes with these study tools. Follow your course’s rules for AI and assessed work.

1 · Learn itunderstand the material

💬AI tutorAsk anything about IS5542 and get step-by-step answers. 📤Explain my notesUpload your slides or lecture and Sia breaks them down. 📑Topic summariserCondense a week into the essentials you actually need.

2 · Practise ittest yourself

📝Practice quiz generatorPractice MCQs and short-answer on any topic. 📊Past paper analysisSee what the exams actually test, topic by topic. ✓Assignment & problem helpWork through problem sets one step at a time.

3 · Revise & cramlock it in before the exam

🃏FlashcardsKey concepts and formulas as a spaced-repetition deck. 📋Cheatsheet maker NEWAuto-build a one-page exam cheatsheet for the course. 🧠Mindmap generatorSee how the topics connect on one visual map.

4 · Discuss itcompare notes

👥Community Q&AAsk other IS5542 students and share what worked.

FAQ

Frequently asked questions

Is IS5542 hard?

Moderate on the six-factor rubric, level with the HK anchor: applied rather than mathematical, but half the grade is a two-hour exam and both halves carry a pass floor.

What is the assessment breakdown?

Participation and exercises 10%, group project 40%, examination 50% (2 hours), per the CityU course syllabus. Minimum passing requirements: 30% coursework, 20% examination.

Can I use GenAI in the assessment?

Yes on the participation exercises and the group project, as the syllabus states; the examination is a written paper.

Do I need to code?

The hands-on exercises design, train and evaluate generative models with popular frameworks and libraries, so expect practical work; no prerequisite is listed.

Which programmes use it?

AI-group core of the MSc Artificial Intelligence in Business; elective in the MSc BDA (IAM), MSc Digital Transformation and Technological Innovation and MSc AI-Driven Innovation.

Is there a textbook?

No compulsory or additional readings are listed in the syllabus.

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