Artificial intelligence in context
- AI, machine learning, deep learning and generative AI.
- Common AI use cases and where AI is already used.
- Capabilities, limitations and common misconceptions.
- How to evaluate an AI use case realistically.
AI fundamentals
Build AI confidence from the ground up
Understand what artificial intelligence and generative AI actually do, then learn to use AI tools clearly, safely and productively in everyday work.
Course overview
A clear, practical introduction to artificial intelligence
AI coverage often swings between hype and confusion. This program gives you a grounded explanation of artificial intelligence, machine learning and generative AI, with an emphasis on what these tools can and cannot do.
You will practise writing prompts, evaluating AI responses and applying AI to research, writing, summarisation and problem-solving. Responsible use, privacy and verification are built into every exercise rather than added at the end.
What makes this program different
- No coding requirement and no assumed technical background.
- Plain-language explanations with real business and workplace examples.
- Practice over theory: each module includes a hands-on task.
- A strong focus on checking output and protecting sensitive data.
Tools disclaimer: Sessions explore tools such as ChatGPT, Gemini, Microsoft Copilot and other relevant platforms only where available and relevant to the batch. No program promises coverage of every AI tool or technology.
Curriculum
AI fundamentals training modules
Eight modules from core concepts to a personal AI workflow.
How generative AI and LLMs work
- Tokens, training data and probability-based output.
- Why models can sound confident and still be wrong.
- Context windows and conversation memory.
- Model types and choosing a tool for a task.
Using AI tools effectively
- Capabilities of common chat-based AI tools.
- Structuring a working session with an AI tool.
- Iterating on output instead of accepting the first answer.
- When to use search, spreadsheets or a human expert instead.
Prompt engineering essentials
- Role, task, context, constraints and output format.
- Providing examples and reference material.
- Breaking complex tasks into steps.
- Diagnosing and improving weak prompts.
AI for research and summarisation
- Generating research questions and search directions.
- Summarising long documents and comparing sources.
- Fact-checking and source verification.
- Avoiding fabricated citations and false confidence.
AI for content and problem-solving
- Drafting, rewriting and adapting content for an audience.
- Brainstorming and structured ideation.
- Using AI to explain difficult concepts.
- Maintaining your own voice and judgement.
Responsible AI, privacy and verification
- Confidential and personal data rules.
- Bias, hallucinations and over-reliance.
- Human review for high-impact decisions.
- Workplace AI policies and acceptable use.
Capstone: your personal AI workflow
- Choose a recurring task from your own work.
- Design a reusable prompt and review checklist.
- Test the workflow and document limitations.
- Present the workflow and lessons learned.
Learning outcomes
What you should be able to do
Explain AI clearly
Describe AI, machine learning and generative AI without jargon.
Choose the right tool
Decide when an AI tool helps and when another approach is better.
Write effective prompts
Provide context, constraints and examples to improve output quality.
Verify results
Check facts, calculations and sources before using AI output.
Protect data
Apply privacy and confidentiality rules when using AI tools.
Build a workflow
Turn a recurring task into a repeatable, reviewed AI-assisted process.
Practical work
Learn by doing, then review what happened
- Build a prompt library for research, writing and summarisation.
- Compare outputs from different prompt structures.
- Fact-check an AI-generated summary against source documents.
- Identify privacy risks in sample workplace scenarios.
- Design and test a personal AI-assisted workflow.
Certification note: This is a practical training program. We provide preparation support for relevant topics where applicable, but we are not an official certification body.
Enrolment questions
AI Fundamentals FAQ
Do I need coding or maths for this course?
No. The program is designed for beginners and focuses on practical understanding and safe use of AI tools, not programming or mathematical foundations.
Which AI tools will I use?
Sessions explore tools such as ChatGPT, Gemini, Microsoft Copilot and other relevant platforms only where available and relevant to the batch. We do not promise coverage of every tool or technology.
Will this course make me an AI engineer?
No. It builds AI literacy and practical usage skills. It is not a machine learning engineering or data science program. For technical work, see AI for Developers.
Can I use my company's documents in exercises?
Only if your organisation permits it and the tool is approved for that data. Otherwise, use sample or sanitised documents. Privacy and confidentiality are covered in module seven.
Is there a certificate?
We provide training and preparation support where applicable. We are not an official certification body, and any third-party certification is booked separately with that provider.
Related AI programs
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AI fundamentals
Start with a strong AI foundation
Tell us your role and current comfort with AI tools. We will confirm the next suitable batch.