Think of it as four nested circles
Artificial Intelligence (AI) is the broadest term. It refers to any software built to perform tasks that normally require human intelligence — understanding language, recognizing images, making decisions. It's a goal, not a specific method.
Machine Learning (ML) is one way of building AI. Instead of a person writing explicit rules, an ML system learns patterns from examples. Almost all AI you interact with today — chatbots, recommendation systems, image recognition — is built using machine learning specifically, rather than older rule-based approaches.
Deep Learning is a specific technique within machine learning that uses layered structures called neural networks. It's the approach behind essentially every major AI breakthrough of the last decade, from image recognition to language models.
Large Language Models (LLMs) are a specific application of deep learning: models trained on huge amounts of text to predict what text comes next. ChatGPT, Claude, and similar chatbots are all built on LLMs.
So the relationship is: AI (broadest) → Machine Learning → Deep Learning → LLMs (most specific, and the reason most people are asking this question in the first place).
Why this distinction actually matters
It's not just trivia. Knowing where a term sits in this hierarchy tells you what it can and can't do:
- Calling something "AI-powered" tells you almost nothing about how it works — a simple rule-based spam filter and a state-of-the-art LLM are both technically "AI."
- Calling something an "LLM" tells you it's specifically text-prediction-based, which explains both its strengths (fluent, flexible language use) and its well-known weaknesses (it can state incorrect things confidently, because it's predicting plausible text, not looking up verified facts).
Generative AI is a separate, overlapping category
One more term worth placing: Generative AI describes AI systems built to produce new content — text, images, audio, code — rather than just classify or predict a number. LLMs are generative AI, but so are image generators like Midjourney, which aren't LLMs at all (they're built differently, on models trained for images specifically).
Go deeper
The AI Glossary has quick definitions for every term mentioned here, cross-linked to related terms. For the full picture with worked examples, AI Fundamentals covers this hierarchy — and what it means practically — from the ground up.
How the terms nest
The cleanest way to hold these apart is as circles inside circles.
Artificial intelligence is the outermost and oldest, covering any attempt to make software do things that seem to require intelligence. A chess program from 1997 is AI. So is a spam filter. The term describes an ambition, not a method.
Machine learning sits inside it: systems that learn patterns from data instead of following rules a programmer wrote. Most AI that works commercially today is machine learning, which is why the terms get used interchangeably even though they are not the same.
Deep learning sits inside that: machine learning using neural networks with many layers. This is the approach behind essentially every AI advance of the last decade.
Large language models sit inside that: deep learning applied specifically to text at very large scale. ChatGPT, Claude, and Gemini are all LLMs.
So every LLM is AI, but very little AI is an LLM.
Terms you will meet alongside these
Generative AI describes what a system produces rather than how it works — anything creating new text, images, audio, or video. An LLM is generative AI; a spam filter is not.
Foundation model refers to a large model trained broadly and then adapted to many uses, as opposed to one built for a single task.
Transformer is the specific architecture nearly all modern LLMs use. When people say a model is transformer-based, they mean it uses this design.
AGI means artificial general intelligence — a hypothetical system matching human ability across most tasks. It does not exist, and anyone describing a current product as AGI is selling something.
Why the distinction is worth having
Mostly it is a defense against marketing. "AI-powered" can mean a sophisticated model or a handful of if-statements, because the term covers both honestly. Knowing the layers lets you ask the useful question — what kind of AI, doing what? — instead of taking the label at face value.
It also helps you read the news accurately. A finding about language models says nothing about image recognition, even though both are deep learning. Headlines routinely blur this. Once the nesting is clear, the blurring becomes obvious.
Our AI Glossary defines each of these terms individually, and AI Fundamentals builds them up in order.