10 AI Terms Everyone Should Know in 2026

Artificial intelligence has developed its own vocabulary remarkably quickly.

Not too long ago, we were asking a chatbot to help write an email. Now, somebody is talking about tokens, context windows, and AI agents as though we all took the same computer science class. We did not.

Fortunately, most AI jargon becomes much less intimidating once someone explains it without using six additional pieces of AI jargon. So, let's do exactly that.

1. Large Language Model

We'll start with the one that gets shortened to LLM, because three words were already too many. A large language model is a type of AI that’s trained on enormous amounts of text data.

It learns patterns in language and uses them to generate responses, answer questions, summarize information, and perform other language-based tasks.

In simple terms, an LLM learns to predict what should come next in a sequence of text.

2. Generative AI

This one is broader. Generative AI describes artificial intelligence that can create new content, including:

• Text

• Images

• Video

• Audio

• Computer code

That's the "generative" part. If you ask an AI tool to write a birthday poem or create an image of a corgi wearing a yellow raincoat, you're in generative AI territory.

3. Prompt

If you've ever typed a question into an AI chatbot, you've written a prompt.

A prompt is simply the request or instructions we give an AI system. "Plan five dinners using what's in my refrigerator" is a prompt.

So is "Make this email sound less annoyed because I am, in fact, extremely annoyed."

4. Hallucination

This one sounds much more exciting than it is. An AI hallucination happens when an AI system produces information that sounds believable, but is wrong or completely fabricated.

That might mean inventing a statistic, study, quote, source, or other detail and presenting it with tremendous confidence.

Which is why "the chatbot said so" isn't quite the same thing as checking a reliable source.

5. AI Agent

A regular chatbot generally responds to what we ask. An AI agent can go further by working toward a goal and using available tools to take action.

Instead of simply suggesting an itinerary, for example, an agent might search for options, organize the information, and complete parts of the task itself.

Basically, AI is moving from, “Answer my question,” toward, “Help me get this thing done.”

6. Deepfake

You've probably heard this one outside the tech world. A deepfake is an image, video, or audio recording manipulated with AI to make something look or sound real when it isn't.

That could mean making someone appear to say something they never said or creating a convincing image of an event that never happened.

It's another reason to be skeptical when something outrageous suddenly appears in our feeds.

7. Training Data

AI doesn't emerge from a computer one morning knowing how to write a limerick and explain compound interest.

Models learn patterns from training data, the information used while they're being trained. Depending on the system, that can involve enormous collections of material.

And that data matters because models learn from what's in it, including its patterns, gaps, and imperfections.

8. Token

At the moment, we're not inserting a coin into ChatGPT. But, AI models don't necessarily process language one neat word at a time. Instead, text gets broken into smaller units called tokens.

A token might be a whole word, part of a word, a character, or punctuation.

That's why AI companies sometimes describe usage limits or pricing in tokens rather than words.

9. Context Window

This sounds like something we'd shop for at Home Depot. A context window is essentially how much information an AI model can work with at one time.

It's measured in tokens, and a larger context window lets the model handle more material in a single interaction.

In human terms, think of it as how much stuff the AI can have spread across its desk at once.

10. Artificial General Intelligence

Finally, we've reached AGI, or artificial general intelligence. AGI generally refers to the idea of AI with broad abilities to learn, reason, and apply knowledge across many different areas.

Here's the important part: there isn't one universally accepted definition or test for AGI.

So, when someone announces that AGI is right around the corner, it's worth remembering that people don't entirely agree on where that corner is.

AI Is Less Complicated Once We Speak the Language

AI isn't going anywhere, and neither is its rapidly expanding dictionary. Fortunately, we don't need to understand neural networks and build one in the garage to follow what's happening.

Once we know our LLMs from our hallucinations, and our prompts from our tokens, a lot of AI chatter starts to sound a lot less mysterious.

The next time someone casually drops "context window" into conversation, we can nod knowingly. And, this time, we'll even know why.

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