Micro Essentials - On Demand
Under the Hood What Drives AI?
AI is everywhere — offering us answers, streamlining our work, and speeding up decisions. But beneath every slick response lies a powerful predictive engine.
If we don’t understand how it works, we can’t properly assess when to trust it, when to question it or when to stop it.
This micro session explains how AI models really work - what drives their outputs, where errors creep in and what’s changing behind the scenes.
From large language models to image generators and new agentic systems, this course will give you the foundational knowledge you need to make sense of the tools now embedded in your daily working life.
Whether you’re shaping AI policy, integrating tools into your workflows or just trying to keep up with your team’s capabilities, this session helps you understand what’s really going on and what you need to do to stay in the driver's seat.
Understanding how AI models work is no longer optional.
If you’re using AI to create, decide, recommend or advise, you need to understand what’s happening beneath the surface.
This course is designed to help you make informed, ethical and confident decisions about the tools you use, the systems you support and the policies you create. You can’t manage AI responsibly without understanding the basics and that’s what this course provides - without jargon, hype or assumptions.
Understand
Understand how large language models and image generators produce outputs
Understand the technical foundations needed to support ethical and effective AI deployment
Learn
How AI models are trained and what shapes their behaviour
How to evaluate the strengths and weaknesses of common AI applications
Identify
Identify the difference between rule-based systems, machine learning, deep learning and agentic systems
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This micro course gives you:
- A clear explanation of how AI models generate responses (text, images, and more)
- A simple overview of model structures, training methods and how different types of AI operate
- Insights into the shift from traditional AI to more autonomous, agentic systems
- An understanding of why AI outputs can be biased, inconsistent or misleading
- Practical knowledge to help you evaluate tools, spot limitations and apply oversight
