What is generative AI — and how does it actually run?

Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.

Covers: 1.1 Terms · 1.2 Uses · 1.3 ML role Format: self-paced, interactive Ends with: 10-question check

Prompt

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Large language model

LLM

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Completion

Key terms, indexed

1.1

Six terms sit at the centre of every generative-AI conversation. Click a card to flip it and read the definition — then flip it back and test your recall.

These terms nest inside one another. Click a ring to see how each field fits inside the next:

Where it runs

1.2

Generative AI is applied across a wide range of personal and organizational tasks, each carrying its own level of success, risk and required oversight. Select a use case to run a live demonstration.

At a glance — oversight level typically associated with each use case:

The engine underneath

1.3

Generative AI is built on machine learning — and specifically on deep learning's layered neural networks. Every model is the product of a repeatable process, not a single act of programming.

1.3.a

Machine learning

The study of computer algorithms that allow computer programs to automatically improve through experience — learning patterns from data rather than following fixed, hand-written rules.

1.3.b

Deep learning

A multi-layered neural network. Each layer extracts a more abstract representation of the data than the one before it, which is what allows generative models to handle language and images so fluently.

raw text → words → phrases → meaning → output

The machine learning process runs through six repeatable stages. Step through them:

Check yourself

Ten questions, objective 1

Score: 0 / 10

Quick reference

GLOSSARY

Every defined term from objective 1, in one searchable list — useful for a last pass before the exam.

No terms match that search.