Generative AI Model Applications Development

Unit code: NIT6008 | Study level: Postgraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
City Campus
VU Brisbane
VU Sydney
Prerequisites
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
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Overview

In this unit, you will learn how to design and build real-world applications powered by Generative AI models, moving beyond experimentation to professional, deployable solutions. Generative AI—especially large language models—can create text, code and structured outputs, but effective use in practice requires careful engineering to achieve reliability, safety and value in specific contexts.

You will develop the skills to frame a problem, select an appropriate GenAI approach, and prototype solutions that integrate with data sources and external tools. You will learn how to improve quality through structured prompting, retrieval-based techniques, evaluation and testing, and you will apply safeguards to manage common risks such as hallucinations, bias, privacy and security concerns.


Learning is hands-on and studio-based. You will work through authentic scenarios and build working prototypes, supported by guided workshops, practical labs, and rapid feedback cycles. By the end of the unit, you will be able to develop and justify a GenAI application design, implement a functional prototype, and evaluate its performance and responsible use in a professional setting.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Analyse and formulate Generative AI application requirements and solution approaches for complex, real-world scenarios.
  2. Design and implement GenAI-enabled application architectures and prototypes that integrate models with organisational data sources and external tools/services.
  3. Synthesise prompt and workflow strategies, including structured outputs, retrieval-supported generation, tool use, to improve reliability, usefulness and user experience.
  4. Critically evaluate GenAI application performance using appropriate evaluation methods and evidence, and refine solutions based on testing outcomes and stakeholder needs.
  5. Critique ethical, legal, privacy, security, bias, cultural and Indigenous data governance risks in GenAI applications, and justify safeguards and responsible deployment decisions.

Assessment

For Melbourne campuses

Assessment type: Laboratory Work
|
Grade: 20%
Build core technical capability through guided, hands-on practice, including basic testing and responsible-use checks..
Assessment type: Project
|
Grade: 50%
Design and develop an end-to-end GenAI-enabled application with Q&A verification, including a brief responsible-use justification.
Assessment type: Case Study
|
Grade: 30%
Demonstrate advanced judgement in evaluating GenAI use in professional contexts, including responsible use and stakeholder impact.

Required reading

Selected readings will be available in VU Collaborate.

As part of a course

This unit is studied as part of the following course(s):

Master of Artificial Intelligence
NMAI | Masters (coursework) degree | Duration: 2 years

Refer to the course page for information on how to apply for the course. VU takes care to ensure the accuracy of this unit information, but reserves the right to change or withdraw courses offered at any time. Please check that unit information is current with the Student Contact Centre.

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