Written by Berna Syuleymanoglu
You use AI more than you probably realize; drafting a message, planning a trip, asking it to fix your typo. But somewhere between “typing a quick question” and “actually knowing what any of these words mean,” most of us never stopped to catch up. And honestly? That’s fair. It moves fast.
So let’s slow it down for a second. At AI4ALL, we believe understanding AI shouldn’t require a computer science degree – just five minutes and a simple breakdown. Here are five of the most common terms you see and hear everywhere, finally explained.
Do not worry! You haven’t missed the boat, and getting good at it is easier than it looks.
Here’s a fun fact: when people say “I asked AI,” they almost always mean Generative AI, but the two terms aren’t the same thing.
AI (Artificial Intelligence) is the big umbrella term for any computer technology that mimics human-like thinking or decision-making. It covers everything from your phone’s face unlock to the spam filter in your inbox.
Generative AI, on the other hand, is a specific type of AI – the kind that creates something new based on a user’s request or prompt: text, images, code, you name it. This is the AI most of us actually interact with day to day, like when you use it to draft an email or brainstorm a birthday message.
LLM stands for Large Language Model. It’s the technology powering tools like ChatGPT, Claude, and many more – a system trained on vast amounts of text so it can predict, respond to, and generate language that sounds remarkably human.
Ultimately, while it gives off that feeling, it doesn’t actually think or search for truth. It simply guesses the single most likely next word to follow your prompt.
If an LLM’s whole job is guessing the next most likely word, your prompt is what steers that guess. It’s the raw material the model draws from to figure out what you actually want back.
A prompt is simply what you type or say to an AI tool: your question, instruction, or request. If you’ve read our last guide on prompting, you already know this one is less about “commands” and more about conversation.
A hallucination is when AI states something false with total, unwavering confidence. It won’t hedge, it won’t flag uncertainty – it’ll just hand you a made-up statistic or a fake source like it’s fact.
Remember how this all works – an LLM predicting the next most likely word, steered by your prompt? This is exactly why hallucinations happen. It isn’t checking facts, it’s completing a pattern. Most of the time that produces something true. Sometimes it just produces something plausible – and that’s a hallucination.
This is why double-checking matters for anything important: a date, a statistic, a name.
You’ve probably said “I use Claude” or “I asked Gemini” – but those are apps, the product you actually open and type into. The AI doing the thinking underneath has its own, separate name: GPT-4, Gemini 2.5, Claude Sonnet. That’s the model.
A common way people explain it: the model is the engine under the hood, doing the actual work. The app is the car – the tool that lets us actually interact with it. Companies can swap the engine without changing the car, which is exactly what happens when an app becomes “smarter” overnight – same app, upgraded model.
There you have the essentials – AI, Gen AI, LLM, prompt, hallucination, model, and app – enough to follow along in almost any AI conversation without nodding blankly. In the next series, we’ll get into the nuts and bolts of it all: machine learning, deep learning, training data.
Until then, if you want to discover more stories, tips, insights from across our network, head over to our blog and check AI4All’s website.
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