So, here’s the thing. Last time, we talked about generative AI — you know, the kind of AI that can spit out poems, essays, or even a recipe for cake you didn’t know you wanted. Today, I want to get into large language models. Sounds a bit intimidating, but don’t worry — we’ll keep it simple.
A language model, at its core, is basically something that guesses words. Yep, that’s it. It tries to figure out what word is most likely to come next in a sentence, based on the words you’ve already got there. Think of it like a friend who keeps finishing your sentences — sometimes they’re right, sometimes they’re hilariously wrong.
Let’s imagine this sentence:
“I wrote to the zoo to send me a pet. They sent me …”
And then there’s a blank. Now, the model doesn’t just randomly throw words in there — it looks at its “vocabulary,” which is just a big list of words it knows. Since we’re talking about a zoo, it’ll probably think of animals: dog, elephant, lion, cat… you get the idea.
What the model actually does is give each word a little score — a probability. So it might say:
- Dog: 0.45 (pretty likely)
- Lion: 0.03
- Elephant: 0.02
- Cat: maybe a bit less
And that’s how it decides. No “car” or “building” here, because those just don’t fit.
Why “Large” Language Models?
The “large” part isn’t about the model being physically big (though, if you stored it on paper, it would fill a warehouse). It’s about parameters — which are like little knobs the model adjusts during training. The more knobs it has, the more patterns it can learn. There’s no magic number where a model suddenly becomes “large,” but the ones we talk about today have hundreds of millions or even billions of these parameters.
But here’s the thing: more parameters doesn’t always mean smarter. If it’s too big, it can actually start memorizing stuff instead of learning general patterns — kind of like that kid in school who could recite the textbook but couldn’t answer a slightly different question.
Picking the Next Word
Once the model has assigned probabilities to all its possible words, it usually just picks the one with the highest score. In our zoo example, “dog” wins. Then it sticks “dog” onto the sentence and repeats the process for the next word.
Eventually, it might decide, “Yep, I’m done here” — and that’s when you see something called an EOS token, which just means end of sentence. So you end up with:
“I wrote to the zoo to send me a pet. They sent me a dog.”
Pretty simple in theory, though the math behind it can get wild.
What Can These Models Actually Do?
Okay, so predicting words sounds… fine, but why is everyone so obsessed with LLMs? Because you can build a lot on top of that skill.
They can:
- Answer questions (from “What’s the capital of France?” to “Explain quantum physics like I’m 5”)
- Write essays, stories, or summaries
- Translate languages (“How are you?” into French becomes “Comment allez-vous ?”)
- Solve puzzles, or at least give it a shot
- Analyse text for sentiment (is this review happy or angry?)
They’re basically like a super flexible writing and thinking tool — but powered by a ton of training data.
The Transformer Bit
If you’ve heard of the “transformer” architecture, that’s the tech behind modern large language models. Transformers are good at paying attention to the right words in a sentence — not just the ones right before the next word. That means they get context better, which is why today’s AI feels so much smarter than older chatbots.
The Scale of It All
Training these things involves feeding them ridiculous amounts of text — books, websites, articles — and letting them learn patterns. The result is a model that can predict text in a way that feels surprisingly human.
Some of the biggest public models we know about have over 500 billion parameters. And that’s just the stuff companies are willing to share. There are probably bigger ones out there that haven’t been disclosed.
But again, size is just one factor. A smaller, well-trained model can sometimes outperform a bigger, clumsy one.
A Quick Recap (Without the Jargon)
Large language models are:
- Fancy word-guessers
- Powered by huge numbers of adjustable parameters
- Good at a bunch of language-based tasks
- Built on transformer architecture for better context understanding
And if you remember nothing else, remember this: they don’t “know” things the way we do — they’re just incredibly good at predicting what comes next, based on patterns they’ve seen before.
In the next part of this series, we’ll look at how transformers actually work under the hood — which is where things get more technical, but also more fascinating.