All about LLMs (aka: what even is ChatGPT’s brain?)

Okay, so you’ve heard the term LLM thrown around and maybe nodded like you totally knew what it meant. But deep down? You're like, “Umm… seriously, what is this thing?”

Let’s fix that.

LLM stands for Large Language Model—and it’s basically the brain behind tools like ChatGPT. It doesn’t have feelings or memories or opinions, but it has “read” a ridiculous amount of stuff from the internet. So when you ask it a question, it uses everything it learned to try and predict the most helpful (or sometimes weird) answer.

In this post, we’ll break it all down in plain English:

• What LLMs are

• Where they get their data from

• How that data is stored

• How new data is added (or isn’t)

• Whether LLMs change based on regions, languages, and cultures

• And why sometimes they say weird or biased stuff (and what’s being done about that)

No jargon. No tech brospeak. Just simple explanations and maybe a few analogies involving pizza.

🧠 What even is an LLM?

A Large Language Model is a type of artificial intelligence that’s trained to understand and generate human language. It works kind of like a really smart, extremely well-read autocorrect.

You type something in, and the LLM tries to guess what should come next—one word at a time. It doesn’t actually understand what you’re saying in the way a person does. It’s just very, very good at predicting what you might want to hear based on what it's seen before.

🌐 Where do LLMs get their data?

LLMs are trained on huge piles of text: websites, books, news articles, Wikipedia, programming code, recipes, Reddit rants, and more. Basically, if it’s public and full of words, it probably helped train an LLM.

This data is collected by scraping the internet and curating large datasets—sometimes open-source, sometimes proprietary. The goal is to give the model a wide view of how humans write and speak.

💾 Where is that data stored?

Once collected, all that text is turned into numbers (called tokens) and fed into a giant neural network during training. The model doesn’t memorize the data word-for-word—it learns patterns, grammar, and associations. The actual data isn’t “stored” in the model like a file folder. Instead, it’s more like muscle memory: the model learns how to talk by practicing with massive amounts of text.

The trained model is then stored on servers (lots of them), and when you use something like ChatGPT, you're interacting with that frozen-in-time snapshot of what it learned.

🔁 Can LLMs learn new stuff?

Not by default. Once an LLM is trained, it doesn’t continue learning. It doesn’t browse the internet or pick up new slang unless it’s been fine-tuned (trained again on newer data) or connected to tools that let it look things up in real time.

Some chatbots use plugins, APIs, or live search to add updated info—but the core model itself isn’t constantly learning.

🌍 Do LLMs change based on region or language?

Yes—and it’s a big deal.

LLMs reflect the data they’re trained on. If most of that data comes from English-speaking parts of the internet (like the U.S. or UK), the model may sound very Western. That’s why prompting in different languages—or using regional slang—can lead to wildly different responses.

Some LLMs are trained specifically on non-English datasets, or fine-tuned to perform better in certain countries. The result? You might get different tones, references, or cultural assumptions depending on the region and language the model was designed for.

⚠️ Why LLMs can be biased (and what we’re doing about it)

Because LLMs learn from human text... they pick up human flaws. Racism, sexism, misinformation—if it’s in the training data, the model might echo it.

Researchers try to reduce this using:

• Filtering bad data during training

• RLHF (Reinforcement Learning from Human Feedback)

• Safety layers that catch harmful output

It’s not perfect, but it’s improving. The goal: useful, fair, non-chaotic robots.

🤖 Final thoughts (aka: LLMs are weird, but kinda wonderful)

So, Large Language Models are basically giant, well-read parrots with internet access. They don’t think, they don’t feel, and they definitely don’t know you—but wow, can they write a breakup text or explain quantum mechanics like a champ.

They’re trained on more words than any of us will read in a lifetime, and all they do is try to guess the next most likely word. That’s it. That’s the whole magic trick. ✨

But just because they’re simple under the hood doesn’t mean they’re not powerful. LLMs are already changing how we write, search, create, and even how we think about thinking.

So the next time someone says “LLM” and looks smug about it, you can be like, “Oh yeah, that’s the big ol’ word prediction brain behind ChatGPT. I read a blog about it. There were pizza analogies.”