"OpenAI has released a new model." "Google's latest model beats the rest." "Which AI model is best for homework?"
The word model is everywhere in conversations about AI, and it's rarely explained. Parents tell me they nod along without being quite sure what it means. That's a shame, because the idea is simpler than it sounds, and once you have it, most AI news starts to make sense.
Quick Answer: An AI model is a complex data structure that learns patterns from huge amounts of text and uses those patterns to generate new text. It's fed words, notices which words tend to appear with which other words, and stores those patterns as numbers. Then it can write new text that's similar to what it learned from. The more text a model is trained on, and the bigger the machines used to train it, the better it gets at spotting patterns and writing well. That's why the most capable ones are called large language models, or LLMs. Companies now compete to build the best one: OpenAI makes the GPT models behind ChatGPT, Anthropic makes Claude, Google makes Gemini, much like Apple makes the iPhone and Samsung makes the Galaxy.
A Model Is a Data Structure That Learns
In computing, a data structure is a way of organising information so a program can store it and use it. A shopping list is a very simple one. A spreadsheet is a more organised one.
An AI model is a data structure too, just an enormously complex one. Instead of storing a list of facts, it stores what it has learned about how language works. Inside, that learning is kept as numbers, often billions of them, tuned so that together they capture the patterns of the text the model has seen.
Nobody types those numbers in by hand. The model works them out itself, by being fed text, which is what people mean when they say a model is "trained".
How a Model Learns: Words and Their Neighbours
At its core, a language model is fed words, lots of them, and it pays attention to which words appear and what surrounds them.
Think about the phrase "peanut butter and ___". You already know the next word is probably "jelly" or "jam", not "elephant". You know that because you've seen those words together many times. A model learns in the same spirit, at a vastly bigger scale. Across millions of sentences, it notices that:
- certain words tend to follow certain other words ("brush your" is usually followed by "teeth")
- words used in similar surroundings often have similar meanings ("cat" and "dog" turn up in many of the same kinds of sentences)
- sentences follow patterns of grammar, tone and style, whether it's a recipe, a news report or a bedtime story
Every pattern it picks up nudges those internal numbers a little. Over enough text, the numbers end up holding a remarkably detailed picture of how language fits together.
Then It Writes Something New
Once a model has learned those patterns, it can use them in reverse. Give it a few words, and it uses what it learned to work out what's likely to come next, then the word after that, and so on. The result is new text, similar in style to what it was trained on, but not copied from any one place.
That's inherently what a model is: something that has seen a lot of text, found the patterns in it, and can produce more text that fits those patterns. Everything an AI chatbot does, from answering a question to writing a poem, comes out of that one ability. We explain the word-by-word process in more detail in how ChatGPT actually works.
Why More Data Makes a Better Model
A model can only learn the patterns it has seen. Train it on a single short story and it will know a few hundred words and write something that sounds a lot like that one story. Train it on a whole library and it has far more to learn from:
- It sees more patterns, so its sense of how sentences fit together becomes sharper.
- Its vocabulary grows, so it can talk about far more topics.
- It generates better text, more fluent, more varied and more useful.
There's a second ingredient: size. A bigger model, with more numbers inside it, can hold more patterns. But bigger models need far more computing power to train, which is why AI companies build huge data centres full of powerful chips. More data and bigger machines have allowed companies to train bigger and bigger models, and each step up has made them more capable. That's where the "large" in large language model comes from.
When Models Became Genuinely Useful: ChatGPT
Language models have been around for years, and researchers had been making them steadily bigger. OpenAI's GPT-3, released in 2020, impressed people in the field, but it wasn't something most families had ever heard of.
That changed on 30 November 2022, when OpenAI released ChatGPT, a chat app built on a model called GPT-3.5. For the first time, a language model felt genuinely workable for everyday people. You could ask it a question in plain English and get a clear, fluent answer back. Within a couple of months it was reported to have reached around 100 million users, and the AI hype as we know it had begun.
The AI Race: Many Companies, Many Models
Since then, many companies have joined the race to build the next best large language model. A few of the big names:
- OpenAI makes the GPT models, the ones that power ChatGPT.
- Anthropic makes the Claude models, which come in different sizes such as Opus, Sonnet and Haiku.
- Google makes the Gemini models, which also power AI features across Google's products.
- Meta makes the Llama models, and xAI makes Grok.
- Companies around the world, such as DeepSeek in China and Mistral in France, build their own too.
It helps to think of it like smartphones. Apple makes the iPhone. Samsung makes the Galaxy. Each brand has its own range, releases a new version every so often, and claims its latest is the best. They're all phones, and they all do broadly the same job, but each has its own strengths.
AI models work the same way. GPT, Claude and Gemini are different brands of the same basic technology. Each company keeps releasing newer, more capable versions, and they regularly leapfrog each other. One useful distinction for parents: ChatGPT is the app, and GPT is the model inside it, just as the iPhone's apps run on the phone underneath.
Why This Matters for Your Child
The model is the brain of every AI tool your child will use. As I explained in where to start if you want your child to learn AI, the tools built around models keep changing, but the idea of a model learning patterns from text stays the same. A child who understands what a model is can make sense of every new AI product, rather than being dazzled by it.
The best way to really understand a model is to build one. In our AI & Python course, students write their own mini language model in Python. They feed it text they choose, a book, song lyrics or a film script, watch it learn which words follow which, and then see it write new sentences of its own. They see first-hand what happens when it has more text to learn from, and why the big companies are racing to build bigger models. It's a small model, but it works on the same idea as the ones behind ChatGPT, Claude and Gemini.
That's the real goal of the course. Students come away knowing how language models are made, with a working mini version of how they think. So when the next shiny AI tool arrives, and it will, they aren't starting from scratch. They know there's a model underneath, they have a good idea of what it's doing, and they can pick the tool up and use it with confidence.
Frequently Asked Questions
What is an AI model, in simple terms?
An AI model is a complex data structure that learns patterns from large amounts of data, such as text, and uses those patterns to produce something new, such as a sentence. A language model learns which words tend to appear together and uses that to write new text.
What does LLM stand for?
LLM stands for large language model. It's a language model trained on an enormous amount of text, with billions of internal numbers, which lets it understand and write language far better than smaller models.
Is ChatGPT an AI model?
Not quite. ChatGPT is an app made by OpenAI. The AI models inside it are OpenAI's GPT models. In the same way, Anthropic's Claude app runs Claude models and Google's Gemini app runs Gemini models.
Which AI model is the best?
It changes often. GPT, Claude and Gemini are all highly capable, and each company releases new versions that leapfrog the others. Like choosing between phones, the best one depends on what you're using it for.
Can kids build their own AI model?
Yes, a small one. In our AI & Python course, students with no coding experience build their own mini language model in Python over 10 lessons. It's far smaller than ChatGPT, but it learns and writes using the same core idea.
Learn How AI Models Work in Singapore
Our AI classes run in small groups of up to 6, in person at our Jurong East studio or live online. Every course starts with a free 45-minute trial lesson, where your child builds their first text generator and sees a model learn for themselves. Book a free trial class.
