An LLM is the brain behind tools like ChatGPT and Microsoft Copilot. Trained on vast amounts of text, it has learned to predict what word or idea comes next, incredibly well.

💡 Think of the world's most well-read colleague. They've read everything and can write in any style. They don't understand in the human sense but their pattern-matching is so good it often feels that way.

Generative AI creates new content, like text, images, code, audio, rather than just analysing existing data. You give it a prompt and it generates something original.

💡 Think of the difference between a spell-checker (finds errors) and a ghostwriter (writes the whole email for you). Generative AI is the ghostwriter.

Neural networks are loosely inspired by the human brain, layers of connected "neurons" that refine signals until an answer emerges. Deep Learning just means the network has many layers.

💡 Think of it like a giant game of telephone but instead of garbling the message, each layer refines it. By the last layer, a blurry photo has been recognised as a cat, or a sentence translated into French.

Machine Learning is how AI actually learns. Instead of a programmer writing every rule, you feed the system thousands of examples and it figures out the rules itself.

💡 Think of a new employee learning to spot a good sales lead. You don't hand them a rulebook, you show them hundreds of past deals and let them spot the pattern. Machine Learning does that, just at enormous speed.

AI is when computers do things that normally need human thinking, like understanding language, recognising faces, making decisions, or learning. It's already around you: spam filters, Netflix recommendations, sat nav.

💡 Think of AI like teaching a dog tricks. You show it "sit" hundreds of times with rewards, and eventually it learns the pattern. AI learns the same way — from millions of examples instead of explicit rules.