The “Simplest” Guide to Large Language Models for non-techies.

If you’ve ever used ChatGPT to write something or asked it a question, you’ve already interacted with an LLM. Because ChatGPT is an LLM under the hood. They are now being used by almost everyone in the world. Did you know that ChatGPT is visited by 123 million people every day?

LLMs are changing lives! Literally. They are helping students to learn anything, help people write emails, reports and even help come up with new ideas! Best part is they do not talk like machines in jargon, they talk like you do or your friends do! OMG! That’s like a sci-fi movie come true!

No exaggeration! LLMs might just be among the most useful, yet complex, technologies humans have created.

How do they actually work? How are they so smart? When you ask for help, why does it help you? Let’s answer these questions in this article. I have tried to explain in simple English as much as I can.

What Is an LLM?

An LLM is like a “black box” which has a large amount of information inside it. The box is designed to take input and give output. Meaning you input a question and it gives you an answer. Why did I say black box? Because they take so much inside them that even the brightest minds who created them struggle to understand what is going on inside.

These models are trained from a massive amount of written texts. So massive it is hard to imagine it. Did you know that ChatGPT was trained on approximately 570GB of text data? This translates to roughly 300 billion words or 3.3 million average sized novels!

LLMs learn and remember these large texts! Are you serious? Yes. They are trained to understand all the knowledge, meaning and information in these texts. That’s why if you ask it any question, it can answer it!

But where do these texts come from?

Introducing “Crawlers”. Think of them like tiny robots that go through every website on the internet. They also go through all the books and articles ever written. The robots download all these texts and store it somewhere.

These large texts may contain unwanted content or duplicates. Engineers write sophisticated programs that cleans these texts. They also make sure that the texts cover a vast amount of topics such as politics, history, science, mathematics, news and medicine . They also cover multiple types of language. Did you know that you can talk to ChatGPT in your native language, not necessarily English? Not only that, they also cover movies and TV Shows. Ask ChatGPT, who was the actor in Jurassic Park and it can answer or ask it to talk like him, it can do that as well. The goal is to feed an LLM nearly all the text and information humans have ever created.

I can not learn an essay for my exams! How can an LLM learn the whole internet? Let’s answer that next!

How Do LLMs Learn?

If you wanted to be like a LLM, you’d have to take billions of tests. What? Yes!

When an LLM is training, it is tested many many times. It gives an answer, and it gets feedback, right or wrong, every single time.

Let’s take an example. Imagine the LLM is learning a nursery rhyme like you did when you were a child.

“Twinkle, twinkle, little…”

The LLM guesses: “car.” That’s wrong. It gets a small punishment. Next guess: “dog.” Still wrong. Another punishment. Finally, it says “star.” That’s right! It gets rewarded.

Just like a child learning the poem, it gets punishment and chocolates. It remembers that “Twinkle, twinkle, little star” is the correct answer. That’s how LLMs learn through billions of guesses and corrections, slowly getting better. That’s a lot of punishment and chocolates!

But you might have a question. LLM is a machine, how can it get punished? That’s a good question to ask. So, let’s take an example.

Christmas Light Game

It’s Christmas tomorrow and you are lighting up your room. You have to arrange the lights in such a way to get a shape. Let’s say a “star” pattern. Also note that each light has its own individual switch. If you turn on the right switches, you get a star!

Imagine this: all the bulbs are arranged on a wall like a checkerboard. The bulbs are randomly on or off, and it looks nothing like a star. It’s a total mess.

To the reader: This example gives you the closest feel of how an LLM learns. It requires some imagination and thinking so please be mindful.

You go through each bulb, guessing whether it should be on or off. Each guess decides whether you will eventually see a star or not.

At first, it’s all trial and error. Your guesses make the mess even worse than before. But you don’t give up. You keep switching. Flip by flip, round by round, the lights start to settle. The wrong bulbs go off. The right ones stay on. Slowly, a pattern begins to form.

After enough tries, something clicks. You step back and look at the wall.

And there it is. A star.

You didn’t build it all at once. You didn’t know which switches to flip. You just kept switching, learning from each try. The lights adjusted themselves with every bit of feedback.

That’s how an LLM learns. It starts with random switches. It makes a guess, checks if it is right, and adjusts. Wrong guesses turn off the wrong lights. Right guesses keep the good ones glowing.

Little by little, the mess becomes the right structure.

So what is the punishment? It’s the wrong switch! And what is the reward (chocolate)? It’s the right switch!

And you were correct! That’s a lot of switches and rewards!

LLMs have something called “parameters.” This sounds very complicated, but they are nothing more than tiny switches that an LLM adjusts every time it makes a guess during its training. When they go through a lot of training, they adjust many parameters correctly. Like the Christmas light example, all the parameters are messy initially, and with a lot of trial and error, a clear pattern of knowledge emerges.

Let’s do a guess? How many parameters does an LLM have?

Modern LLMs typically possess billions of parameters. For example, GPT-3 has 175 billion parameters. Tiny models have 1 to 10 billion parameters. That’s way too much than hundreds of switches you had for Christmas.

Another guess. How many times they might have switched these parameters? That is definitely in trillions!

Let me give you another important point. The parameters for LLMs are not simply direct on or off. They can be set to any value between on or off. That’s like saying, 1% on or 90% on. This gives them a high degree of control and precision. Wow, that’s a lot of work! Yes, it is! I did not want to tell you this earlier because it will be hard for you to grasp.

There are certain surprising abilities that appear when models reach gigantic size. Those are emergent abilities like basic reasoning or coding that are not intended but that show up as the model increases in size. Let’s talk about this next!

Emergence of Knowledge

You’ve done it. You lit up a star on your wall of bulbs. It took time, but after enough tries, enough switch flips, the pattern became clear.

Now something interesting happens. Your friend walks in and says, “Hey, can you make a mountain instead?”

You pause.

You’ve never made a mountain before. No one told you how. But something inside you clicks.

You start flipping switches again, not randomly this time, but with intuition. You’ve seen how lights behave. You’ve felt what patterns look like. You understand how switches shape the wall.

And before long, there is a mountain on the wall. Wow!

This is what emergence feels like.

When a model gets big enough, and its huge number of parameters are trained well enough, it doesn’t just repeat what it saw. It starts to generalize. It starts to figure out new patterns and new shapes. Even if it was never directly trained for them.

At smaller sizes, a model might only make stars, if that’s all it saw. But when it’s large enough, and trained deeply enough, it can start making mountains, trees, animals, or anything you ask not because it memorized them, but because it learned how to build.

That’s emergence!

It’s not magic. It is not a guess. It’s the result of switching so many tiny switches, again and again, that the model starts to understand patterns, not just repeat them.

And that’s why big models feel smart. They don’t just light up. They adapt. They shape meaning out of light.

Emergence is real and it changed everything!

Emergence is a real phenomenon that researchers observed in large language models like GPT-3, PaLM, and Gopher. In 2022, a paper titled “Emergent Abilities of Large Language Models” showed that some abilities suddenly appeared when models became large enough. These were not gradual improvements but sharp jumps in skills like arithmetic, translation, and code generation.

For example, smaller models completely failed to solve simple math problems. But once a model reached a certain size, it suddenly started solving them with high accuracy. The performance graph was flat for a long time, then suddenly jumped upward. This surprise jump is what researchers call an emergence. It means the model figured something out internally that it couldn’t do at smaller scales.

This discovery changed the direction of AI development. Instead of trying to teach smaller models to do more, researchers realized that scaling up was the key to unlocking new abilities. Today’s largest models are built with this idea in mind, reaching hundreds of billions or even trillions of parameters to trigger more powerful and flexible behaviors.

Researchers today are studying when and why emergence happens, what tasks show it, and how to measure or predict it. They are also building tools to better understand what is happening inside these large models.

Transformers: The Real Star

Sorry for the technical word, but I promise to keep it simple. And no, these are not the robots from the movies. Transformers are one of the biggest inventions in AI, and without them, LLMs like ChatGPT wouldn’t exist. They completely changed how machines understand and generate language.

Let’s go back to your wall of lights.

Until now, you were flipping switches one by one, learning patterns through trial and error. It worked, but it was slow. What if you had a friend helping you who could instantly tell you which switches matter the most for the shape you’re trying to make?

That friend is the transformer.

Transformers help the model figure out, “Which words or pieces of information are important right now?” Instead of reading everything word by word, transformers look at the whole sentence at once and decide which parts to focus on. It’s like having a smart guide who says, “Forget that bulb in the corner and focus on this one. It’s key to making the star.”

Thanks to transformers, the model doesn’t waste time flipping random switches. It learns faster, remembers better, and understands context in a way that older models simply couldn’t.

So yes, transformers are the reason LLMs became so smart. They turned a slow guessing game into a focused, intelligent system that can light up the right patterns in a flash.

Short story of transformers

The story of transformers begins in 2017, when a group of researchers at Google published a paper with a bold title: “Attention Is All You Need.” At the time, AI models were slow and struggled to understand long sentences or hold onto context. Older methods processed words one at a time, like a robot reading a book letter by letter. The transformer changed that. It introduced a way for the model to look at all the words at once and decide which ones were most important. That simple idea, called attention, helped models learn faster, understand better, and grow to enormous sizes. That one paper quietly changed the future of AI, and nearly every powerful language model today, including ChatGPT, is built on top of it.

Inference: The Magic Moment

You’ve built the star. You’ve trained the model. Now comes the fun part: using it.

This stage is called inference, and it happens every time you interact with an AI model like ChatGPT. Whether you’re asking a question, writing a paragraph, or generating ideas, the model is now ready to respond.

Remember your wall of lights? Think of it like this. After all the switching and fixing, you finally get the perfect shape. You take a photo of it. Now, instead of building the pattern from scratch every time, you just look at the snapshot and follow the same steps to recreate it. Fast and easy. So when someone asks you to build a star again, you already know what to do by following the saved pattern.

That’s exactly how inference works. The model is no longer learning. It is done with training. It simply looks at what you say, matches it with patterns it already knows, and gives you an answer.

This part is super quick. What took months or even years to train can now respond in seconds. The heavy lifting has already been done. Inference is like using all that learning to deliver something useful instantly.

Wrapping It All Up

Let’s take a moment to reflect.

You started by wondering how a machine like ChatGPT can be so smart. Now you know.

LLMs are built by feeding them huge amounts of text, training them through billions of guesses and corrections, adjusting tiny switches called parameters, and guiding them with tools like transformers to learn patterns in language.

After all that hard work, the model reaches inference. That’s when it becomes ready for you to use, ready to respond, ready to help.

From nursery rhymes to transformers, from random guesses to emergence, from learning to performing, LLMs are truly one of the most amazing tools humans have ever created.

They are not magic. But they are close.

So the next time you ask ChatGPT a question, remember: behind that simple reply is a wall of glowing lights, trained, tuned, and ready to shine just for you.