How Large Language Models like ChatGPT Work

You're likely interacting with AI (Artificial Intelligence) more and more, perhaps using tools like ChatGPT for drafting emails, summarizing reports, or even brainstorming ideas. These Large Language Models (LLMs) have become remarkably capable, but how do they actually work? What's happening behind the screen when you type a prompt and receive a well informed response?

This post aims to provide a high-level, conceptual understanding of the core mechanics behind LLMs. It's designed for the tech-interested who want to grasp the fundamentals without needing a deep dive into complex mathematics or code. Think of it as looking under the hood to see the main components, not rebuilding the engine. We'll explore how these models process language, how they "learn," and what's actually happening during that near-instantaneous generation of text.

The Building Blocks: Turning Words into Numbers

At their core, computers operate on numbers - not the usual complexities of human language. The first step for an LLM is to convert text into a format it can understand: numbers. This involves two key concepts Vocabulary and Tokenization.

An LLM has a predefined, fixed dictionary called a Vocabulary. This isn't just a list of words; it includes common words, punctuation, and often parts of less common words (sub-words). The size varies - smaller models might have 30k-50k "tokens" in their vocabulary, while large, multilingual models can exceed 100k. (See an example here.)

Tokenization is the process of breaking down your input text (the prompt) into these predefined tokens from the vocabulary. For example, the word "unbelievably" might be split into tokens like "un", "believe", "ably". Each token in the vocabulary has a unique numerical ID. So, your sentence becomes a sequence of numbers that the model can mathematically process.

You might wonder how the model handles words with multiple meanings, like 'bank' (river side) versus 'bank' (financial institution). In most modern LLMs, the Tokenization process itself is primarily based on the text's characters and frequency statistics, not its meaning. Therefore, the word string 'bank' would typically be assigned the same initial token ID (or sequence of sub-word IDs) regardless of its intended meaning in the sentence. The crucial step happens inside the neural network: through mechanisms like attention, the model analyzes the surrounding Context. This allows it to generate vastly different internal representations (called contextual embeddings) for that 'bank' token ID depending on whether nearby words relate to finance or geography. The model learns this ability to interpret context during its massive Training phase.

The Core Task: Predicting What Comes Next

Surprisingly, the fundamental task of most LLMs is simple to state: predicting the next token. Based on the sequence of tokens it has seen so far (the Context), the model calculates the probability for every single token in its vast vocabulary of being the next one in the sequence.

Think of it like an incredibly sophisticated auto-complete feature. It considers the context – the words and sub-words that came before – to make the most statistically likely prediction for what should follow. Complex abilities like answering questions, summarizing text, or translating languages emerge from repeatedly performing this next-token prediction, stringing together the most probable sequence of tokens that fulfill the prompt's request.

Inside the Model: Layers, Weights, and Fixed Knowledge

So, how does the model make these predictions? It uses a complex structure inspired by the human brain: an artificial neural network with many layers. Input tokens (as numbers) enter the network and are processed through these layers. Each layer performs mathematical transformations on the data passed from the previous one, allowing the model to analyze the sequence and identify increasingly complex patterns and relationships between tokens – even those far apart in the text. This ability to weigh the importance of different parts of the context is crucial for understanding meaning and generating relevant text.

The "intelligence" or capability of the model resides in its parameters, which are primarily Weights and Biases. These are millions, often billions, of numerical values associated with the connections between the artificial neurons in the layers. They act like tuning knobs, determining how information flows and is transformed throughout the network. When you hear a model has "7 billion parameters" or 7b - it's mostly these weights and biases.

Crucially, after the initial training phase (which we'll discuss next), these weights and biases are fixed! The trained LLM is like a final, incredibly complex mathematical function or a compiled piece of software. Its knowledge and abilities are encoded within these parameters and do not change during typical use (called Inference). Giving it a prompt is like feeding inputs into this fixed function; it doesn't learn or adapt on the fly.

Learning the Patterns: The Immense Task of Training

How do these billions of parameters get their values? Through a massive, one-time Training process. The model is fed enormous amounts of text data – potentially large portions of the internet, books, articles, code, and more, known as Datasets.

During training, the model is repeatedly given sequences of text and asked to predict the next token. Its prediction is compared to the actual next token in the training data. An "error" value is calculated based on the difference. Then, using sophisticated optimization algorithms, the model's weights and biases are minutely adjusted to slightly reduce this error. This cycle of predicting, comparing, and adjusting is repeated trillions of times across the vast dataset.

This training process is computationally immense, requiring specialized hardware (like thousands of GPUs or TPUs) running continuously for weeks or months. It's a major reason why developing foundational LLMs is incredibly resource-intensive and expensive. (For an analogy of the complete lifecycle see How AI Models work, explained with Cars.)

Through this intensive process, the model doesn't "understand" language like humans do. Instead, it learns intricate statistical patterns, correlations, grammatical structures, facts (as represented in the data), and even writing styles, all encoded within the final, fixed values of its weights and biases. Critically, the patterns it learns directly reflect the data it was trained on. If the data contains Bias, inaccuracies, or predominantly certain viewpoints, the model will inevitably learn and potentially replicate them in its outputs.

Putting It All Together: From Prompt to Generated Text (Inference)

This is how the journey from prompt to model response (Inference) looks like:

1. Prompt: You provide your input text.
2. Tokenize: The text is broken down into tokens with numerical IDs.
3. Initial Context: These tokens form the starting context sequence.
4. Network Pass: The sequence is fed into the neural network with its fixed weights and biases.
5. Predict Probabilities: The model calculates the probability of every token in its vocabulary being the next one.
6. Select Token: A token is chosen. Often it's the most probable one, but to make outputs less repetitive and more "creative," a controlled amount of randomness (often adjusted by a setting called Temperature) can be introduced, allowing less probable (but still plausible) tokens to be selected sometimes.
7. Append to Context: The chosen token is added to the end of the current context sequence.
8. Repeat: This updated, longer context sequence is fed back into the model (Step 4) to predict the next token. This loop continues, generating the response one token at a time.
9. Stop: The process stops when the model generates a special "end-of-sequence" token, reaches a predefined maximum length, or fulfills another stopping condition. Models have a Maximum Context Length (Context Window) – they can only effectively "remember" or consider a certain number of recent tokens when making predictions.
10. De-Tokenize: The sequence of generated numerical token IDs is converted back into human-readable words and sentences.

Summary: Sophisticated Patterns, Not True Understanding

Large Language Models like ChatGPT are fascinating engineering. They operate by tokenizing language, processing it through layered networks governed by billions of pre-learned weights and biases, and predict the next token in a sequence based on context. Their apparent knowledge comes from statistically learning patterns in vast amounts of text data during an intensive, costly training phase, after which their core parameters are fixed.

Understanding these basics helps appreciate both the power and the limitations of these AI tools. They are incredibly sophisticated pattern-matching and generation engines, excellent at manipulating language based on the data they've seen. They are not, however, thinking or understanding entities in the human sense. Keeping this conceptual framework in mind can help users leverage these tools effectively while maintaining realistic expectations about their capabilities and potential pitfalls, like inherited biases.