Ever read an article about AI and felt like everyone skipped the part where they explain the words? You are not alone. Terms like “large language model,” “tokens,” and “multimodal” get tossed around as if we all agreed on their meaning at some meeting nobody was invited to.

So here are the main AI terms explained in plain English, all in one place. No math, no jargon for its own sake. Just the words you keep seeing, what each one means, and a quick example. I work with websites and online tools every day, and most of these ideas are simpler than they sound once you strip away the buzzwords. Where a term has its own full guide on the site, I’ve linked it so you can go deeper whenever you want. For a much bigger technical version, Google keeps a detailed machine learning glossary too.

AI terms explained: start with the big picture

Artificial intelligence (AI). Software that does things we usually think need human intelligence, like understanding language, recognizing images, or making a decision. Your spam filter is AI. So is the app that suggests the next word as you type. Here is what AI actually is in simple terms.

Machine learning (ML). The main way modern AI is built. Instead of a person writing every rule by hand, you show the system thousands of examples and it learns the patterns itself. Show it enough photos of cats and it learns to spot one. This is machine learning explained more fully.

Deep learning. A powerful type of machine learning that uses layered networks to handle messy, real-world data like images, sound, and text. It powers voice assistants and the vision in self-driving cars. If the overlap confuses you, this guide sorts out AI vs machine learning vs deep learning.

Neural network. The structure behind deep learning. It’s a web of connected units, loosely inspired by how brain cells pass signals along, arranged in layers that each hand information to the next until an answer comes out. More in what a neural network is.

The words behind chatbots

Generative AI. AI that creates new content rather than only sorting or labeling what already exists. Text, images, music, code, it can produce them from a request. ChatGPT drafting an email is generative AI at work. Start with what generative AI means.

Large language model (LLM). The engine inside chatbots like ChatGPT, Gemini, and Claude. It’s trained on huge amounts of text, and at its core it predicts the most likely next chunk of text based on what came before. That one idea, done at enormous scale, is what lets it write and answer. See what a large language model is.

Token. The small piece of text a model reads and writes, usually a word or part of a word. Models measure their input and output in tokens, and many tools price their usage that way. A common word might be one token, while a rare one gets split into two or three. Here is how tokens work.

Transformer. The model design that made today’s chatbots possible. Google researchers introduced it in a 2017 paper called “Attention Is All You Need,” and nearly every large AI model since has been built on it. It’s the “T” in GPT. More in what a transformer is.

How AI models are made

Training data. The examples a model learns from, often text and images pulled from many sources. The range and quality of that data shapes what a model is good at and where its blind spots are. Weak data in, weak answers out.

Parameters. The internal values a model adjusts while it learns, a bit like millions of tiny dials it keeps tuning to get better. When you hear a model has “billions of parameters,” that is a rough measure of its size, though bigger does not always mean smarter.

Fine-tuning. Extra training that takes a general model and specializes it for one job, such as customer support or legal language, using a smaller focused set of examples. It’s far cheaper than building a model from scratch.

Talking to AI day to day

Prompt. Simply what you type to an AI: your question, instruction, or request. Clearer prompts get better answers, and it’s a real skill worth practicing. We have a full guide on writing better AI prompts.

Hallucination. When AI gives you an answer that sounds confident but is wrong or invented, like citing a source that does not exist. It happens because the model predicts plausible text, not verified facts. For a user, this is the single most important term to understand. Here is why AI hallucinates and how to catch it.

Tip: treat AI as a fast first draft, not a final answer. Check anything that matters, especially names, numbers, dates, and links, against a trusted source before you rely on it.

The words you’ll see in the news

AI agent. An AI that does more than chat. It takes steps to complete a task, like searching, filling in a form, or booking something, often with some independence. Think of it as an assistant that can take actions on your behalf. See what AI agents are.

Multimodal AI. AI that works with more than one kind of data at once: text, images, audio, and video together. It’s why you can show a chatbot a photo and ask about it, or talk to it out loud. As IBM explains, early chatbots handled text only, while newer models mix inputs and outputs.

Reasoning model. A model that takes a moment to work through a problem in steps before it answers, which helps with math, logic, and multi-part questions. It trades a little speed for more careful answers. More in AI reasoning models explained.

Artificial general intelligence (AGI). The headline term. It means a hypothetical future AI that could match or beat humans across almost any task, not one narrow skill. We are not there. As IBM puts it, AGI is still a “hypothetical stage,” and experts do not even agree on how we would know we had reached it. Today’s tools are impressive, but they are narrow specialists.

Common Questions

Do I need to memorize all these AI terms? No. Skim them once, then come back when a word trips you up. You will pick up the common ones, like AI, prompt, LLM, and hallucination, just by using the tools for a week.

What is the difference between AI, machine learning, and deep learning? Picture nested circles. AI is the big idea, machine learning is the main way we build it today, and deep learning is a powerful type of machine learning. The full comparison is here.

Is AGI here yet? No. Today’s AI is “narrow,” meaning it’s strong at specific tasks but cannot flexibly do everything a person can. AGI is still a goal and a debate, not something you can download.

Final takeaway

You do not need a technical background to follow the AI conversation. Once you know that a model predicts tokens, that a prompt is just your instruction, and that a hallucination is always possible, most headlines stop reading like a foreign language. Bookmark this page, keep it open the next time you read about AI, and use the linked guides when you want the deeper version. The jargon was the hard part, and you have just gotten past most of it.