What Is an LLM? How ChatGPT Actually Works

ChatGPT can answer questions, write code, summarize documents, translate languages, analyze images, and help complete complex tasks.

But what is happening behind the screen?

ChatGPT is powered by a type of artificial intelligence called a large language model, or LLM.

An LLM does not think exactly like a human. It does not search through a giant database of prepared answers. Instead, it processes your input, recognizes patterns, and generates a response one small piece at a time.

This article explains how that process works.

1. What Does LLM Mean?

LLM stands for large language model.

The name has three parts.

Large means the model contains a very large number of adjustable values called parameters and is trained using enormous amounts of computing.

Language means it is designed to process and generate human language.

Model means it is a mathematical system that has learned patterns from training data.

An LLM can perform many language-related tasks because it has learned relationships between words, phrases, facts, programming syntax, writing styles, and ideas.

2. An LLM Is a Neural Network

At its core, an LLM is a very large artificial neural network.

A neural network contains layers of mathematical operations. Each layer transforms the information it receives and passes the result to the next layer.

The network does not contain normal software rules such as:

If the user asks this question, return this answer.

Instead, it learns statistical relationships during training.

For example, it may learn that the words “CPU,” “instructions,” “registers,” and “cache” frequently appear in related technical discussions.

These relationships allow the model to generate useful responses to questions it has never seen in exactly the same form.

3. Text Becomes Tokens

Before an LLM can process text, the text must be divided into smaller units called tokens.

A token may be:

• A complete word
• Part of a word
• A number
• A punctuation mark
• A short sequence of characters

For example, a sentence such as:

The GPU renders an image.

might be divided into tokens representing “The,” “GPU,” “renders,” “an,” “image,” and the final period.

Tokens are the basic units that an LLM reads and generates. OpenAI describes tokens as the building blocks of text processed by its models.

4. Tokens Become Numbers

A computer cannot directly understand the meaning of a word.

Each token is therefore converted into a collection of numbers called a vector or embedding.

These numbers represent learned relationships between tokens.

Tokens with related meanings may develop similar numerical representations. For example, the representations of “CPU” and “processor” may be closer than the representations of “CPU” and “banana.”

The model also needs information about token order.

Consider these two sentences:

The dog chased the cat.
The cat chased the dog.

They contain almost the same words, but their meanings are different because the order has changed.

The model must therefore represent both the tokens and their positions in the sequence.

5. The Transformer Architecture

Most modern LLMs are built using a neural-network architecture called the Transformer.

The Transformer was introduced in the 2017 research paper Attention Is All You Need. Unlike earlier architectures that processed sequences mainly one step at a time, the Transformer relies heavily on attention mechanisms and supports much greater parallelism.

Transformers are made from many repeated processing blocks.

Each block usually contains two important components:

• An attention mechanism
• A feed-forward neural network

Information passes through these blocks repeatedly, allowing the model to build increasingly useful representations of the text.

6. What Is Attention?

Attention helps the model determine which earlier tokens are important when processing the current token.

Consider this sentence:

The server could not start because it had lost power.

To understand the word “it,” the model must determine that “it” probably refers to the server.

Attention allows the model to compare tokens and assign different levels of importance to them.

It does not simply look at the nearest word. It can use relationships between tokens across a much longer passage.

Modern Transformers normally use multi-head attention. This means several attention operations examine the same text in parallel, potentially focusing on different relationships.

One may focus on grammar. Another may focus on subject relationships. Another may help track technical context.

7. What Are Parameters?

An LLM contains a large collection of adjustable numerical values called parameters or weights.

Parameters control how information moves through the neural network.

At the beginning of training, these values are not useful. The model produces poor or nearly random predictions.

During training, the system repeatedly adjusts the parameters so that the model becomes better at predicting text.

The knowledge of the model is therefore distributed across many numerical relationships.

It is not normally stored as a simple collection of documents or a table containing one answer for every possible question. Training primarily changes the model’s parameters to represent patterns found in the training material.

8. How an LLM Is Trained

The first major training stage is commonly called pretraining.

The model receives a sequence of tokens and attempts to predict a missing or following token.

Suppose the training text contains:

The operating system manages the computer's ...

The next token might be “hardware,” “memory,” “resources,” or something else that fits the context.

The model produces probabilities for many possible tokens.

If its prediction is poor, a training algorithm calculates the error and adjusts the model’s parameters slightly.

This process is repeated across enormous numbers of examples.

Over time, the model becomes better at:

• Grammar
• Sentence structure
• Writing styles
• Programming patterns
• Relationships between concepts
• Predicting useful continuations

The objective sounds simple: predict the next token.

However, becoming good at that task requires the model to learn a surprisingly large amount about language and the patterns represented in its training data.

9. Learning to Follow Instructions

A pretrained language model may be able to complete text, but that does not automatically make it a useful assistant.

It must also learn to follow instructions.

One method is supervised fine-tuning. Human-written examples show the model how a helpful assistant should respond to different requests.

Human evaluators may also compare several answers and indicate which response is better.

OpenAI’s InstructGPT research described a process that combined demonstrations, human rankings, a reward model, and reinforcement learning from human feedback. This improved instruction following compared with relying on pretraining alone.

Modern systems may use several training and evaluation techniques, and the complete training process for current commercial models is not publicly disclosed.

10. What Happens When You Send a Prompt?

When you enter a message into ChatGPT, the system first prepares the information the model needs.

At a simplified level, the process looks like this:

Your message
      ↓
Tokenization
      ↓
Token embeddings
      ↓
Transformer layers
      ↓
Probability distribution
      ↓
Next token
      ↓
Repeat

The model examines your prompt together with other available context.

That context may include:

• Earlier messages in the conversation
• Instructions that guide the assistant
• Information from uploaded files
• Results returned by tools
• Relevant memory, when enabled

The model then calculates a probability for many possible next tokens.

It selects one token, adds it to the response, and repeats the calculation.

This continues until the answer is complete.

Although the result appears as a complete paragraph, it is generated incrementally.

11. Why Can an LLM Hallucinate?

An LLM generates a likely continuation based on patterns and context.

It does not automatically verify every statement against an authoritative source.

This can produce a hallucination: an answer that sounds confident and well written but contains incorrect or invented information.

Hallucinations happen for several reasons:

• The training data may be incomplete
• The prompt may be ambiguous
• The model may combine related patterns incorrectly
• The required information may be too recent
• The model may generate plausible language without enough evidence

This is why important claims should be checked against reliable sources.

Giving the model access to search, documents, databases, calculators, or specialized tools can improve accuracy, but tools do not guarantee that every conclusion will be correct.

12. ChatGPT Is More Than an LLM

An LLM is the language-processing engine.

ChatGPT is the complete product built around one or more AI models.

A useful analogy is:

The LLM is the engine. ChatGPT is the complete vehicle.

Depending on the task and available features, ChatGPT may combine a model with:

• System instructions
• Safety mechanisms
• Conversation history
• Memory
• Web search
• File analysis
• Image understanding
• Code execution
• External tools

OpenAI describes ChatGPT as an AI-based service capable of working across text, images, audio, tools, research, analysis, and multi-step tasks.

The exact internal architecture and routing system used by current versions of ChatGPT are proprietary. Therefore, no public explanation can describe every internal step.

But the fundamental process remains understandable:

Convert input into tokens.
Process the tokens with neural-network layers.
Use attention to interpret context.
Calculate probabilities for possible outputs.
Generate the response token by token.

This process requires enormous amounts of matrix computation.

That is why GPUs and specialized AI accelerators are so important. Their parallel arithmetic units and high-bandwidth memory can perform the large matrix operations used during LLM training and inference.

An LLM is not a database, a search engine, or a human brain.

It is a trained mathematical model that has become extremely capable at recognizing and generating patterns.

Conclusion

A large language model is not a conventional database, a traditional search engine, or a human brain.

It is a mathematical model trained on enormous amounts of data.

Through parameters, vectors, Transformers, and attention mechanisms, an LLM learns complex patterns and relationships between words, concepts, facts, and ideas. It then uses the available context to generate a response one token at a time.

Understanding the following fundamental concepts provides the foundation for learning more about artificial intelligence and large language models:

• Tokens
• Vectors and embeddings
• Parameters
• Transformers
• Attention mechanisms
• Pretraining
• Fine-tuning
• Inference
• Hallucinations

Together, these concepts explain how an LLM processes language, learns patterns, and generates useful responses.

In the next article, we will examine tokens more closely and explain how tokenization affects context length, performance, generation speed, and cost.