AI Basics Explained: How Large Language Models Like ChatGPT Work

If you're using ChatGPT and don't even know how it works, you've come to the right place. Let's break it down so you can understand what makes this AI so powerful. How does AI, like ChatGPT, manage to understand your questions and generate such human-like replies? The answer lies in the amazing world of Large Language Models (LLMs). ChatGPT is actually one of them (Chat Generative Pre-trained Transformer). These are a type of AI that learns by using a huge amount of text data and special training methods to understand and create responses that sound like they came from a real person. These models have changed the way we interact with technology, making it possible to have natural conversations with machines. Let’s dive into how these models work, explained in simple steps.

What Are Large Language Models?

Large Language Models, or LLMs, are a type of artificial intelligence designed to understand and generate text. LLMs basically work by calculating the next most probable word. How do they do this? First, they take a look at almost all the English literature available on the internet. This means they read tons of books, articles, websites, and more. By doing this, they learn what words are likely to come next based on a certain sequence of words. For example, if I ask you (the reader) to try to predict my next word, and I start by saying, "Here we go...", you are probably going to guess the sentence "here we go again." Or if I say, "I have a ...", you might guess the famous phrase "I have a dream," or maybe you guess "I have a girlfriend." No matter what you guess, your brain is looking for what you have heard the most—in other words, the most probable wording sequence. LLMs work in a similar way. They have studied everything we wrote on the internet and can now mathematically calculate the next best word to use. While writing this, I searched on Google for the most used English sentence, and it returned: "How Are ...". That's it—you guessed it, it's "How are you." Now, you can think like an LLM!

Think of an LLM as an incredibly well-read assistant. It doesn’t have personal experiences or emotions, but it has "read" so much text that it can provide information, answer questions, and even create stories. By learning from all this data, LLMs can become very useful tools that can talk about almost any topic.

How Do LLMs Learn: The Training Process

The magic behind LLMs, like ChatGPT, starts with two main phases: pre-training and fine-tuning.

1. Pre-training: During this phase, the model is exposed to a large amount of text data to learn general language patterns, grammar, and even factual information. This process involves turning words into numbers, a bit like giving each word a special code that helps the model understand what the word means and how it relates to other words. During pre-training, the model learns to predict the next word based on previous words, just like how we try to guess what comes next in a sentence. This helps the model understand language patterns and use them to create responses that make sense.Imagine you’re learning a new language. You don’t know much yet, but you keep hearing the same phrases over and over. Even if you don’t fully understand the language, you start to pick up what words usually come next. Like if you hear, "Buenos días, ¿cómo...", you might guess "estás" comes next because you’ve heard it before. LLMs are like that—they pick up on patterns by studying lots of examples, just like we do when we start recognizing phrases in another language.You can think of pre-training like the model's basic education. It’s like when a person reads a lot of books to gain general knowledge. The model builds up a huge store of language patterns, facts, and ideas that it can use to create responses. However, because the data comes from the internet, it can sometimes include biases or mistakes, which is why further training is needed. Those who have tried it know, but for those who haven't, try asking ChatGPT about Barack Obama and then about Donald Trump—those kinds of biases are the result of the data it's trained on.

But what does "reading" mean for an LLM? When an LLM "reads" text, it first converts the words into numbers through a process called embedding. Embeddings are like turning each word into a set of coordinates that represent its meaning in a way the computer can understand. Once the words are turned into embeddings, the model can then calculate the probability of what words are likely to come next. This process allows the LLM to generate responses that seem natural and make sense based on the text it has learned from. Essentially, it's about taking what it has "read" and figuring out, mathematically, what the next best word should be.

2. Fine-tuning: After pre-training, the model goes through fine-tuning, where it’s trained on more specific data, often with the help of human reviewers who provide feedback. This makes the model's responses more accurate, helpful, and safe. Fine-tuning is like giving the model extra coaching to make sure it behaves in a way that meets human expectations.During fine-tuning, human reviewers might rate the model's responses, which helps guide the model to give better answers. This phase is super important for making the model ready for real-world use, where the quality and safety of responses really matter. It’s why ChatGPT can give not only relevant information but also do so in a friendly and conversational way.

How Does ChatGPT Work?

Let’s use ChatGPT as an example to understand how an LLM works in real time. When you type a question or a prompt, ChatGPT analyzes what you wrote and breaks it down into smaller pieces called "tokens" (these are like individual words or parts of words). Then it uses its training to predict the best possible response based on the context you gave. It does that by calculating the probability of each word being next to each word and then comparing it to all the text it has read.

The model doesn’t just look at the most recent word or phrase; it considers the entire conversation to give a coherent answer. This ability to keep track of context is what makes ChatGPT feel like a real conversation partner. It’s why it can answer follow-up questions or understand when you refer to something you said earlier.

For example, if you start by saying, "Tell me about space travel," and later ask, "How long does it take to get to Mars?" ChatGPT understands that "it" refers to space travel to Mars. This context awareness is one of the key features that makes LLMs good at having human-like conversations.

Generative AI: Creating Meaningful Content

Generative AI means that models like ChatGPT can create new content—whether that’s text, images, or even music. In the case of ChatGPT, it generates text by selecting words one by one in a way that makes sense, based on everything it has learned. This process happens super fast, allowing the model to give you responses almost instantly.

What makes ChatGPT so powerful is its ability to create content that seems meaningful and makes sense in context. It’s like having a conversation with someone who has read millions of books and articles—it can pull from all that information to give you an answer that’s relevant, informative, and sometimes even creative.

Generative AI isn’t just for answering questions. It can also help with creative tasks, like writing poems, drafting emails, coming up with story ideas, or even composing music. This versatility is what makes generative AI so exciting for many different industries, from entertainment to customer service.

Breaking Down the Complexity

• Vast Training Data: LLMs are trained on billions of words, which gives them a wide base of knowledge. This training data includes many different topics, which is why ChatGPT can talk about everything from cooking recipes to quantum physics. The huge amount of data helps the model be flexible and knowledgeable, making it useful in lots of situations.
• Pattern Recognition: These models are really good at recognizing patterns in language. They don’t truly understand meaning like humans do, but they can mimic understanding by picking up on patterns and using them to give appropriate responses. For example, if you ask about a historical event, the model can recognize the pattern of the question and provide an answer based on similar text it has seen before.
• Context Awareness: One of the key features of LLMs is their ability to remember the context of a conversation. This means they can understand follow-up questions and provide answers that make sense based on earlier parts of the conversation. Context awareness is super important for giving responses that feel natural and coherent, especially in longer conversations where multiple topics might come up.

Why Are LLMs Important?

LLMs like ChatGPT are a big step forward in making technology more interactive and easy to use. They help us write emails, brainstorm ideas, learn new things, and even provide companionship through friendly conversation. Understanding how they work helps us appreciate both what they can do and their limitations—like the fact that they can sometimes make mistakes or generate information that sounds believable but isn’t totally correct.

These models are being used more and more in different applications, from customer support chatbots to virtual assistants that help manage our schedules. They have the potential to transform industries by automating repetitive tasks, improving customer service, and even helping with education by providing personalized tutoring. The possibilities are huge, and as LLMs keep getting better, their impact on our daily lives will grow even more.

However, it’s also important to recognize that LLMs have limitations. They aren’t perfect and can sometimes give incorrect or biased information, especially if the data they were trained on has mistakes or biases. This is why human oversight is often needed when LLMs are used in important applications.

Final Thoughts

Large Language Models are powerful tools that bring us closer to interacting with machines in a natural, human-like way. By learning from lots of text and using advanced algorithms to create responses, they help bridge the gap between human language and computer understanding. ChatGPT is just one example of how far we’ve come, and as these technologies get even better, they’ll become more useful in our daily lives.

From helping us draft emails to answering complex questions, LLMs like ChatGPT are becoming a key part of how we work and communicate. As we move forward, understanding the basics of how these models work will help us use them better and make informed decisions about their role in our society.

If you found this explanation helpful, stay tuned for the next post in our AI Basics series. We'll explore the different types of AI models, how they work, and how each of them plays a role in shaping the future of technology—like powering autonomous agents to perform specific tasks or enabling voice AI assistants to interact with us more naturally.