No jargon, no background needed – just the handful of words that unlock most conversations about AI.
Agent – AI that can go and do tasks for you – click, search, take steps – not just answer in a chat.
AI (artificial intelligence) – A broad term for machines doing things that need intelligence when people do them. So broad it's almost meaningless on its own – always ask which kind.
Bias – Skew an AI inherits from its training data or design – worth asking about wherever it affects real people.
Chatbot – The app you actually talk to (ChatGPT, Claude, Gemini). A friendly wrapper around a model.
Generative AI – AI that creates new things – writing, pictures, audio, code – rather than just recognising or sorting. The kind behind the recent boom.
Hallucination – When AI makes something up but says it with total confidence. The reason to double-check anything that matters.
LLM (large language model) – The technology behind chatbots like ChatGPT – trained to predict the next word across enormous amounts of text.
Machine learning – Systems that learn patterns from examples instead of being programmed with fixed rules. The foundation under most of what we call AI.
Model – The trained “brain” itself, as distinct from the app wrapped around it.
Multimodal – AI that also handles images, audio, or video, not just text – it can “look” and “listen”, not only read.
Prompt – Whatever you type to ask the AI something. Clearer questions get better answers.
Training data – The text and images an AI learned from, frozen at a cut-off date – the source of both its knowledge and its blind spots.