You’ve probably heard people talk about LLMs, short for Large Language Models, especially since tools like ChatGPT became popular. But what exactly is an LLM? And how does it actually work?
Whether you’re tech-savvy or just curious, it’s totally normal to wonder how these models do what they do. I’ve been diving into research papers about LLMs, and I want to break down what I’ve learned in a way that’s simple and easy to understand. (If you’re new to the topic, you may want to start with Chapter 1: What Is Generative AI?)
In this post, I’ll answer three key questions:
- What is an LLM?
- How does an LLM work?
- Can we train our own LLM?
Let’s explore these together: no complex jargon, just plain English.
1. What Is an LLM?
LLM stands for Large Language Model, a type of artificial intelligence (AI) model that understands and generates human-like text. If you’ve ever chatted with ChatGPT, used auto-complete in your email, or asked your virtual assistant a question, there’s a good chance an LLM was behind the scenes helping out.
But what makes it “large”, and what makes it a “language model”?
Breaking it down:
- Language Model:
At its core, a language model is a system trained to understand text and predict the next word. Imagine typing a message and your phone suggests the next word: that’s a basic language model at work. LLMs do the same thing at a much, much larger and more advanced scale. - Large:
“Large” refers to the sheer size of the model: we’re talking about billions (or even trillions) of parameters. Parameters are like the “settings” the model learns during training. The more it has, the more patterns it can recognize in language.
LLMs are trained on massive amounts of text, such as articles, websites, books, and conversations, so they can learn the meaning, structure, and flow of language. After training, they can answer questions, summarize text, write code, translate languages, and even carry on meaningful conversations.
Think of it like this:
An LLM is like a super-intelligent text buddy that has read most of the internet. It doesn’t know things the way humans do, but it’s incredibly good at recognizing patterns in text and continuing them in a way that makes sense.
Real-world examples of LLMs:
- ChatGPT by OpenAI
- Gemini (formerly Bard) by Google
- Claude by Anthropic
- LLaMA by Meta
These models are used in everything from customer support chatbots to writing tools to search engines. They have quickly become part of our everyday tech.
2. How Does an LLM Work?
Now that we know what an LLM is, let’s look at how it actually works. Don’t worry, we’ll keep it simple and skip the heavy math.
Step 1: Learning from Massive Text Data
LLMs learn by reading. And not just a few books: we’re talking about huge amounts of text from websites, books, news articles, Wikipedia, and more. This process is called training.
During training, the model looks at countless examples of how words appear together in sentences. It doesn’t memorize exact content. Instead, it learns patterns like:
- Which words often appear together?
- Which words usually follow others?
- How do sentences start and end?
It does this by adjusting millions (or billions) of tiny internal settings called parameters. These are what help it “guess” the next word.
Step 2: Predicting the Next Word
At its heart, an LLM is just trying to predict the next word in a sentence. That’s it!
If you type:
“The cat sat on the…”
The LLM might suggest:
“mat”
Because it has seen that phrase many times during training. But if the sentence is:
“The scientist presented her findings at the…”
It might suggest:
“conference”
Because that makes more sense in that context.
Over time, the model becomes incredibly good at guessing the next word, and that allows it to write full sentences, paragraphs, even pages that sound natural and coherent.
Step 3: Fine-Tuning (Optional)
After the initial training, some LLMs are fine-tuned for specific tasks like customer support, legal writing, or programming. This just means they’re given more focused examples to make them better at particular skills.
Step 4: Responding to You
When you ask a question (like in ChatGPT), the model takes your prompt and starts generating a response one word at a time, using everything it learned during training. It doesn’t “think” or “understand” like a human, but it’s surprisingly good at creating answers that sound thoughtful and helpful.
A Quick Analogy:
Think of an LLM as autocomplete on steroids. It doesn’t know facts the way humans do, but it’s trained on so much text that it can sound like it knows almost anything, because it has seen so many examples of how people write and speak.
3. Can We Train Our Own LLM?
Short answer: Yes, but it’s not easy, or cheap.
Training your own LLM is like trying to build your own rocket. It’s possible, but it takes a lot of resources, time, and expertise. That said, you can understand how it works, and even explore smaller-scale versions.
Let’s walk through what it takes.
Step 1: Collecting the Dataset
An LLM learns from datasets, which are huge collections of text. They can include books, articles, web pages, dialogues, code, and more. The more diverse and high-quality the dataset, the better the model will perform.
Examples of datasets:
- Common Crawl: a snapshot of billions of web pages
- Wikipedia: for general knowledge
- BooksCorpus: a collection of books
- OpenWebText: high-quality content from Reddit links
Important: you can’t just throw in random text. You want data that’s clean, useful, and ethically sourced (with proper licenses and privacy considerations).
Step 2: Tokenization (Turning Words into Numbers)
Computers don’t understand words; they understand numbers. So before training, the text is tokenized.
Tokenization is the process of breaking text into small pieces called tokens. A token might be:
- A word:
"apple" - A subword:
"ap","ple" - Even just a letter or symbol:
"a",".","!"
Each token is converted into a number. These numbers are what the model actually “reads” and learns from.
Example:
Input: "The cat sat."
Tokens: ["The", "cat", "sat", "."]
IDs: [101, 209, 337, 13]The specific numbers depend on the vocabulary the model was trained with.
Step 3: Choosing a Model Architecture
Once you have tokenized data, you need to feed it into a model architecture, the blueprint of how the AI works. Most modern LLMs are based on a type of neural network called the Transformer (introduced by Google in 2017).
Transformer models are really good at handling long texts and understanding the relationships between words, even when they’re far apart in a sentence.
Popular architectures and model families include:
- GPT (by OpenAI): good for generating text
- BERT (by Google): good for understanding and analyzing text
- LLaMA (by Meta): an efficient, openly available family of LLMs
Step 4: Training the Model
This is where things get really heavy.
Training an LLM means feeding it tokenized text over and over again, letting it learn which words tend to follow others. It adjusts its internal parameters (think of them as memory or intuition) to get better at predicting the next word.
But here’s the catch:
- Training a small model (around 100 million parameters) might take days on a high-end GPU.
- Training a large model (like GPT-3, with 175 billion parameters) takes millions of dollars, specialized hardware, and a team of researchers.
That’s why most people don’t train full LLMs from scratch.
Step 5: Fine-Tuning (The More Realistic Option)
If full training is like building your own car engine from scratch, fine-tuning is more like customizing a car someone else already built.
Fine-tuning means starting with a pre-trained model (like GPT-2 or LLaMA) and continuing to train it on a smaller, more specific dataset, such as legal documents, medical texts, or customer service conversations, to specialize it.
Fine-tuning lets you:
- Make a model better at a certain task
- Train it to follow your brand’s tone or guidelines
- Reduce bias or improve accuracy in a specific domain
You can fine-tune models using popular frameworks like:
- Hugging Face Transformers
- PyTorch
- TensorFlow
And you can use pre-trained models from open model libraries to get started without needing a supercomputer.
So… Can You Really Train One?
Yes, if:
- You’re working on a small or medium-sized model
- You use open-source tools and pre-trained models
- You have access to good GPUs (or use cloud services like AWS, GCP, or Paperspace)
Probably not, if:
- You’re aiming to build something like ChatGPT from scratch with zero infrastructure. The cost and complexity are extremely high.
Final Thoughts
Large Language Models are one of the most exciting technologies today. They may seem like magic, but behind the curtain, it’s all about data, pattern recognition, and smart math.
You don’t have to be a data scientist to appreciate how they work, and if you’re curious, you can explore building or fine-tuning smaller models yourself.
Stay curious, and who knows? Maybe your next project will be powered by your very own LLM.
To try this myself, I fine-tuned a pre-trained GPT-2 model on a news dataset: tokenizing the data, training with the Hugging Face Trainer, and generating news-style text from prompts. You can read more on the project page or browse the code on GitHub (HoaSens LLM).
References:
[1] What are Large Language Models?, https://aws.amazon.com/what-is/large-language-model/
[2] How long does it take to train an LLM?, https://milvus.io/ai-quick-reference/how-long-does-it-take-to-train-an-llm
