AI comes with a lot of jargon, and it can make the whole topic feel harder than it is. This glossary explains 15 common terms in everyday language. Bookmark it and come back whenever you hit a word you don’t recognize.
The basics
Artificial Intelligence (AI)
Computer systems designed to do tasks that normally need human thinking, such as understanding language, recognizing images or making suggestions.
Machine Learning
A way of building AI where a system learns patterns from lots of examples instead of following only hand-written rules. Think of teaching by showing, not by explaining every step.
Generative AI
AI that creates new content, such as text, images, audio or code, rather than only analyzing existing data. Most popular chatbots fall into this category.
Model
The trained system that produces the answers. When people say “a new model came out,” they mean a new version of the underlying AI.
Talking to AI
Prompt
The instruction or question you give an AI tool. A clear prompt usually gets a better answer than a vague one.
Chatbot
A program you interact with through conversation. You type, it replies, and you can keep going back and forth.
Context
The information the AI can “see” during your conversation, including your messages and anything you’ve pasted in. More relevant context usually means more relevant answers.
Token
A small chunk of text, often part of a word, that AI reads and writes in. Tools often have limits measured in tokens, which is why very long inputs can get cut off.
How AI behaves
Large Language Model (LLM)
The kind of AI model behind most text-based chatbots. It’s trained on huge amounts of text so it can understand and generate language.
Hallucination
When an AI states something false or invented, often in a confident tone. It’s why you should always verify important facts.
Bias
Unfair or skewed tendencies in AI output, usually reflecting imbalances in the information the system learned from. It’s a reason to review results critically.
Training Data
The information used to teach a model. The quality and variety of this data affects how well, and how fairly, the AI performs.
Newer ideas you’ll hear about
AI Agent
An AI system that can take a series of actions toward a goal, such as researching, drafting and organizing, rather than just answering a single question. The level of independence varies by tool.
Multimodal
Able to work with more than one type of input or output, such as text, images and audio together. For example, you could upload a photo and ask questions about it.
Fine-Tuning
Giving an existing model extra training on specific material so it performs better at a particular job, such as answering questions in one industry’s terms.
Quick-reference table
| Term | In one line |
|---|---|
| AI | Computers doing tasks that need human-like thinking |
| Machine learning | Learning patterns from examples |
| Generative AI | AI that creates new content |
| Prompt | Your instruction to the AI |
| Token | A small piece of text the AI processes |
| Hallucination | A confident but false answer |
| AI agent | AI that takes multiple steps toward a goal |
| Multimodal | Handles text, images, audio, and more |
Why these terms matter
You don’t need to memorize all of this. But knowing a handful of terms helps you understand news stories, compare tools and write better prompts. “Hallucination” in particular is worth remembering, because it’s the reason checking facts is part of good AI use.
Final thoughts
Every field has its jargon, and AI is no different. Behind the buzzwords are fairly simple ideas. Start with the basics here, and add new terms as you come across them.
