MM1041 Chap.8 Generative AI and Prompt Engineering
Generative AI and Prompt Engineering
What generative AI is
In the course module, generative AI is AI that produces new material, whether text, pictures, program code or sound, by drawing on patterns it has learned from data that already exists. Generative AI chatbots, with ChatGPT and Gemini named as examples, understand prompts and reply in natural language.
The module treats using them well as an everyday workplace skill, and the published schedule gives generative AI three sessions, ending with its application in business.
What people use it for
The module lists the common uses: explaining ideas, summarising information, drafting emails, brainstorming options, rewriting text, generating code and helping to organise thoughts.
Each of these is useful in a business course, and each needs a different check afterwards, because a fluent reply is not necessarily a correct one.
Prompting as a method
The module's video covers how chatbots can help at work, a five-step prompt framework and how to put it into action, four methods of iterating on a prompt, creating images, and using generative AI responsibly.
The general lesson is that the first prompt is rarely the last: a clear task, context and format produce a better first draft, and critical reading of each output shapes the next prompt.
Where generative AI comes from
The e-learning lesson on machine learning shows an early example of generation: a model that turns a video of a horse into a realistic video of a zebra, learned without labelled examples.
Generative AI grew out of the same idea of learning patterns from large amounts of data and producing new content that fits them.
The rules in this course
The course outline allows generative AI tools in the written tasks, the individual and group assignments, with a clear declaration of the use.
The first-week slides add that writing an assignment in another language and using AI to translate the whole work is forbidden. The module also links the University's student guidance on generative AI and a PolyU platform, reached with a University login, that offers text and image models.
What this chapter covers
- 01
Generative AI creates new content from learned patterns
- 02
Chatbots, prompts and natural-language replies
- 03
Workplace uses from summaries to code
- 04
Prompting and iteration
- 05
Evaluating output critically
- 06
Declared use in the written assessments
Worked example · free
Plan a declared use of generative AI
- 2Give two legitimate uses.
- 2Describe a check on the output.
- 2Explain how you comply with the rule.
Key terms
- Generative AI
- Artificial intelligence that creates new text, images, code or audio from patterns in existing data.
- Prompt
- The instruction or question a user gives a generative AI system.
- Prompt Engineering
- Designing and refining prompts so that a generative model produces clearer, more useful output.
- Chatbot
- A program that holds a conversation in natural language, answering prompts in text.
- Iteration
- Revising a prompt after reading the output, to tighten, redirect or extend the result.
Generative AI and Prompt Engineering FAQ
Am I allowed to use ChatGPT for the group project?
Yes, with a condition. The course outline permits generative AI in the written tasks, including the individual and group assignments, provided the use is clearly declared. The first-week slides also forbid drafting in another language and having AI translate the whole work, so write in English and state which tools your group used and for what.
Why is chatbot output risky to copy into an assignment?
Because a chatbot generates the most plausible continuation of a prompt rather than checked facts. It can invent figures, companies or references in fluent, confident prose. The module asks you to evaluate outputs critically, which in practice means checking every claim before it appears in your work and treating the reply as a draft to be tested.
What makes a prompt better?
A clear task, enough context and a stated format usually improve the first answer, and iterating improves the rest: read the output, then tighten the request, redirect it or ask for alternatives. The course video presents its own five-step framework and four iteration methods, so learn those in the course's words for any question that asks about them.
Exam move
Take one short task from another chapter, such as explaining overfitting, and write three versions of a prompt for it, each more specific than the last. Compare the replies and note what changed. Then write a two-sentence declaration of that use as you would for an assignment.
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