Beginner AI Glossary

A set of rules or step-by-step instructions that a computer follows to solve a problem. AI algorithms learn from data to improve their outputs over time.

Computer systems designed to perform tasks that normally require human intelligence — such as recognizing speech, translating languages, making decisions, and generating content.

A method where systems improve by learning patterns from data, without being explicitly programmed for every case. Most modern AI uses machine learning.

A computational model loosely inspired by the human brain. Layers of connected nodes process data and help AI systems recognize patterns in images, text, and audio.

An AI model trained on enormous amounts of text data. LLMs understand and generate human language at a sophisticated level. ChatGPT, Claude, and Gemini are all LLMs.

AI systems that create new content — text, images, audio, video, or code — based on patterns learned during training. The fastest-growing category of AI in recent years.

The instruction, question, or input you give to an AI system. The quality of your prompt directly affects the quality of the output.

When an AI generates incorrect or fabricated information with apparent confidence. A known limitation of current language models. Always verify important AI outputs.

AI designed for one specific task — like translating text, recommending products, or filtering spam. All AI tools available today are narrow AI.

The branch of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation tools, voice assistants, and text summarization.

The dataset used to teach an AI model. The quality, diversity, and size of training data heavily influence how well — and how fairly — the AI performs.

Training a pre-existing AI model on additional specific data to improve its performance for a particular task or domain — for example, adapting a general model for legal or medical language.

An AI system that can autonomously take actions, use tools, and complete multi-step tasks — rather than just responding to a single prompt. An emerging frontier in practical AI deployment.

The process of running a trained AI model to produce an output. When you submit a prompt to ChatGPT and receive a response, that response is generated through inference.