AI terms, without the jargon.

Technology that enables software to perform tasks that normally require human judgment, recognition, prediction or decision-making.

AI that creates new content — such as text, images, audio, video or code — based on patterns learned from existing data.

A type of AI model trained on large amounts of text so it can understand and generate human-like language.

An AI system that can work toward a goal, make decisions and take actions using tools, data or connected systems.

An approach where AI can plan and carry out multi-step work with a degree of autonomy instead of only answering one prompt at a time.

The practice of giving AI clear instructions, context and constraints so it produces more useful and reliable results.

A method that lets an AI model retrieve relevant information from a trusted knowledge source before generating its answer.

Using technology to move repetitive tasks and information through a process automatically, with people involved where needed.

An AI assistant designed to answer questions using an organization’s approved documents, information and knowledge sources.

A database designed to store and search information by meaning and similarity rather than only exact words or traditional fields.

A design approach where people review, approve or guide important steps instead of leaving every decision entirely to AI.

The rules, responsibilities and controls that guide how AI is selected, used, monitored and managed inside an organization.

An AI application that helps a person with tasks such as finding information, drafting or reviewing work.

Automation that uses AI for a step requiring interpretation, generation or a prediction within a defined workflow.

A system in which multiple AI agents coordinate, divide responsibilities or pass work to one another.

A model’s request to use a defined function or tool, with execution controlled by the surrounding application.

A protocol for exchanging tools and context between compatible AI applications and servers.

The practical conditions an organization needs to use AI for a particular task responsibly and effectively.

A structured way to check whether an AI system’s outputs and actions meet defined requirements for its intended task.