How ChatGPT works?

In the world of technology, there are some moments that truly make an impact and change the way we think about digital platforms. One such moment happened with the release of ChatGPT. Launched by OpenAI, this application took the world by storm, crossing 1 million users in just 5 days and setting a new record for the fastest-growing platform. By 2024, it had garnered a staggering 200+ million users, making it one of the most talked-about technological innovations in recent years.

In this article, we will delve deeper into the inner workings of ChatGPT.

What is ChatGPT?

ChatGPT is a chatbot developed by OpenAI, designed to have human-like conversations with users. The idea behind it is that it can understand natural language—like the way humans talk—and generate meaningful, relevant responses. Whether you're asking it a question, seeking advice, or even requesting a creative story, ChatGPT is able to comprehend your input and provide helpful replies. It can do this because it has been trained on vast amounts of text data, enabling it to "learn" patterns in the way language works. However, the real magic happens when you understand how it’s built and trained.

What is a Large Language Model (LLM), and How Does it Work?

The core of ChatGPT is what’s known as a Large Language Model (LLM). But what does that mean?

At its core, an LLM like ChatGPT is a machine learning model that predicts the next word in a sequence. This might sound simple, but it’s incredibly powerful. The way it works can be understood as a classification task in machine learning. Imagine you’re reading a sentence like, “The cat likes to sleep in the __.” The model’s job is to predict what word comes next. Based on its training data, it predicts the word "box" because that makes the most sense. In a nutshell, this is what the model does—it takes a sequence of words as input, analyzes patterns, and predicts the most likely next word.

To achieve this, the model doesn’t just predict a single word in isolation; it looks at the surrounding words, their context, and applies statistical patterns learned from a huge dataset. Over time, the model improves its predictions, and this ability to predict the next word extends to complex conversations.

Breaking Down the Meaning of GPT

You’ve probably heard the term GPT when talking about ChatGPT. But what does GPT stand for?

Generative: This refers to the model’s ability to generate new text. Rather than just selecting from pre-written responses, it creates responses on the fly based on the input it receives.

Pretrained: The model has already been trained on a massive corpus of text before it’s put to use. This pretraining involves exposing the model to large amounts of data so it can understand the structure of language, grammar, and context.

Transformer: This is a type of deep learning architecture that’s used to process sequences of data. Transformers are particularly effective for tasks involving language because they can look at all parts of a sentence simultaneously, rather than in a linear sequence. This allows the model to understand context more efficiently and generate more accurate predictions.

4 Phases of Training a Large Language Model

Training a large language model like ChatGPT isn’t a one-step process. It involves several phases that allow the model to improve its accuracy and ability to follow instructions. Let's take a deeper look at each of these four steps

Step 1: Pre-training Phase

The first phase is pre-training. In this stage, the model is exposed to a huge amount of text data from books, articles, websites, and more. During pre-training, the model learns the basics of language—how words relate to one another, grammar, context, and meaning.

However, while the model gets better at predicting the next word, there’s a limitation. The pre-training data doesn't emphasize instruction-following. The structure of a typical conversation—where a question is asked, and the model responds accordingly—is not very common in the text the model has been trained on. As a result, the model might struggle when it comes to following instructions accurately.

Step 2: Supervised Fine-Tuning (SFT)

To overcome this limitation, the next step is Supervised Fine-Tuning (SFT). In this phase, high-quality instruction-response pairs are curated by contractors—people who design specific prompts and their ideal responses. The model is then trained to predict the next word, but this time, using data that includes structured prompts (questions or instructions) and the correct responses.

The key here is that the model is learning how to interpret and respond to instructions. The model doesn’t just predict the next word based on general patterns; it's now being trained on how to generate a response that makes sense within the context of a question or instruction. This fine-tuning allows it to improve its performance in conversational settings.

Step 3: Reward Modeling

After supervised fine-tuning, the model enters the reward modeling phase. Here, the fine-tuned model generates multiple possible responses for a given prompt. Contractors rank these responses, selecting the best one. Then, the model is tasked with predicting the ranking of these responses. This prediction is compared with the actual rankings given by the contractors, and the model is penalized or rewarded based on how well its predictions align with the rankings.

This step helps the model refine its ability to provide high-quality responses, and it helps the model understand which kinds of answers are considered the best. This is a key step in making the model more reliable and useful in real-world applications.

Step 4: Reinforcement Learning

The final step in the training process is Reinforcement Learning (RL). In this phase, the model generates multiple possible answers for a prompt, similar to reward modeling. But instead of just ranking the answers, the model receives rewards based on the quality of the answers.

In reinforcement learning, tokens (words) in the response are reinforced based on how much reward they contribute to the quality of the response. This allows the model to refine its output further and learn how to generate even better responses, continuously improving over time.

Conclusion

As AI continues to shape how we interact with technology, understanding tools like ChatGPT becomes increasingly important. By exploring how ChatGPT predicts and generates responses through training phases like pre-training, fine-tuning, and reinforcement learning, we can appreciate the complexity behind its ability to have natural conversations. This technology is transforming industries and improving the way we engage with machines.