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AI is an umbrella term, not a single technology. Generative AI is one category within it; retrieval-augmented generation (RAG) adds information from a separate source when a model responds; and an AI agent is generally discussed in terms of goals, actions and autonomy. An “AI system” can also have a specific legal meaning: the EU AI Act defines it for that regulation, not for every technical or policy discussion.
What does “artificial intelligence” mean?
Artificial intelligence (AI) is a broad term for machine-based systems that perform tasks such as producing predictions, recommendations or decisions. The exact definition depends on the source and purpose. NIST’s glossary, for example, lists multiple definitions drawn from different source documents, including formulations centered on human-like tasks and systems that act rationally.
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That variation matters: a technical description, a policy document and a law may use “AI” differently. When a definition has legal consequences, check which law and jurisdiction it belongs to rather than assuming it is a universal technical definition.
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Generative AI (GenAI) is a class of AI models that emulates the structure and characteristics of input data to generate derived synthetic content. NIST’s glossary gives examples including text, images, video and audio, as well as other digital content.
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“Generative” describes a capability or category; it does not mean all AI is generative. AI systems can also produce predictions, recommendations or decisions without generating the kinds of content associated with GenAI.
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) combines a generative model with a separate retrieval system or knowledge base. When a user asks a question, the retrieval component identifies relevant information and supplies it to the model as context for a response.
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Because the information is retrieved at query time, the material supplied to the model can change without retraining the model. That describes how RAG works; it is not a guarantee that retrieved material is complete, relevant or correct, or that the resulting answer is accurate.
What distinguishes an AI model from an AI system?
An AI model is the component whose learned parameters are used to produce or help produce outputs. An AI system is the broader machine-based arrangement that operates in a particular setting and produces outputs that may affect people or an environment. A model can be part of a system, but the terms are not interchangeable.
The distinction is important in practice: a model’s capabilities alone do not describe how it is connected to inputs, what outputs it produces, or how those outputs are used. The European Commission AI Act Service Desk poses this same question in its guidance. For the legal definition of “AI system” under the EU AI Act, see the next section.
What does “AI system” mean under the EU AI Act?
The EU AI Act defines an AI system as a machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment. It infers from inputs how to generate outputs—such as predictions, content, recommendations or decisions—that can influence physical or virtual environments.
This is a legal definition for the EU AI Act, not a universal replacement for technical definitions. The European Commission AI Act Service Desk says the text it presents reflects the consolidated version dated 27 July 2026. That date qualifies the cited wording; readers applying the law should consult the relevant current legal text and guidance for their situation.
What are AI agents and agentic AI?
AI agent and agentic AI are evolving industry terms, not perfectly settled technical categories. The European Commission AI Act Service Desk says “AI agent” is not legally defined in the AI Act and is often used inconsistently. It describes broad agreement around an agent receiving and processing environmental input, then executing actions that may affect its environment.
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The Commission summarizes the terminology problem this way: “The term ‘AI agent’ is often used inconsistently in public debate, due in part to a blurry and still evolving demarcation between AI agents and other kinds of AI.”
An OECD paper published on 13 February 2026 identifies three elements that recur across definitions: objectives, outputs (often actions), and autonomy. The formulations still differ, so those elements are useful clues rather than a definitive checklist or legal test. Do not assume that a product described as “agentic” has a particular level of autonomy or ability to act; the term alone does not establish its behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do these AI terms differ?
| Term | What it describes | Key distinction |
|---|---|---|
| AI | A broad category of machine-based systems and capabilities. | Definitions vary by source and purpose. |
| Generative AI | A category of models that generates derived synthetic content. | A subset of AI, not a synonym for all AI. |
| RAG | A setup that supplies retrieved information to a generative model at query time. | External information can change without retraining the model; retrieval does not assure correctness. |
| AI model | A component associated with learned parameters. | Not the same as the full system in which it is used. |
| AI system | A broader machine-based arrangement; under the EU AI Act, a term with a specific legal definition. | Meaning depends on context, especially whether the EU law is being discussed. |
| AI agent / agentic AI | Evolving terms commonly associated with objectives, outputs or actions, and autonomy. | Usage is inconsistent and the terms are not legally defined in the EU AI Act. |
Where can you look up more AI terminology?
NIST’s The Language of Trustworthy AI: An In-Depth Glossary of Terms, published on 29 March 2023, is a named reference for AI terminology. Its existence does not make every term’s meaning fixed across technical, policy and legal contexts; check the source and purpose behind a definition when precision matters.
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