Overview

Large Language Models, or LLMs, are AI systems designed to understand and generate human language.

ChatGPT is powered by an LLM. The model is trained by reading huge amounts of text and learning patterns in language, such as grammar, facts, writing styles, and common relationships between ideas.

The basic idea is:

1. Text is split into small pieces called tokens.
2. Each token is converted into numbers the model can process.
3. The model studies the context.
4. The model predicts the most likely next token.
5. Repeating this prediction creates a full answer.

An LLM does not think exactly like a human. It generates responses by using patterns learned from data.

Machine Learning

In the LLM world, machine learning is the process of training a model to predict language patterns from huge amounts of text.

The model is not given hand-written rules for grammar, facts, or reasoning. Instead, it sees many text examples and learns by trying to predict the next token.

Input:  The capital of France is
Target: Paris

At the beginning, the model’s predictions are mostly wrong. During training, it compares its prediction with the correct next token and calculates how wrong it was. This error is called loss.

prediction -> compare with target -> calculate loss -> update model weights

The model has many internal numbers called weights. Training adjusts these weights again and again so the model becomes better at predicting the next token.

Over time, next-token prediction teaches the model useful language patterns:

• which words usually appear together
• how sentences are structured
• how ideas relate to each other
• how questions and answers work
• how different writing styles sound

A Transformer is the machine learning architecture used by many modern LLMs. It helps the model understand relationships between tokens by using attention.

Attention lets the model decide which tokens are most important for the current prediction. For example, in the sentence “The dog chased the ball because it was excited,” attention helps the model connect “it” with “dog.”

Deep Learning

Deep learning is a type of machine learning that uses neural networks with many layers.

Each layer transforms the information a little more. Early layers may learn simple patterns, while deeper layers can learn more abstract ideas.

For language, this means a deep learning model can learn things like:

• spelling patterns
• grammar
• sentence structure
• meaning
• relationships between concepts
• tone and style

In an LLM, deep learning helps the model turn text into useful internal patterns, then use those patterns to predict the next token.

Neural Network

A neural network is a system made of connected layers that process numbers.

The simple flow looks like this:

input -> layer -> layer -> layer -> output

Each layer receives numbers, transforms them, and passes the result to the next layer.

In an LLM:

1. Text becomes tokens.
2. Tokens become numbers.
3. Neural network layers process those numbers.
4. The final layer produces probabilities for the next token.

You can think of a neural network as a pattern-finding machine. It does not store every sentence it has seen. Instead, it learns useful patterns from many examples.

Embedding

An embedding is a numerical representation of text.

Computers cannot directly understand words like humans do, so words and tokens are converted into numbers.

A token is a small piece of text. A token can be:

• a whole word
• part of a word
• punctuation
• a short character sequence

For example, a sentence might be split like this:

"I love machine learning" -> ["I", "love", "machine", "learning"]

After tokenization, each token is mapped to numbers. These numbers are called embeddings.

Embeddings help the model understand similarity and meaning. For example, the embeddings for “king” and “queen” may be closer together than the embeddings for “king” and “banana” because “king” and “queen” are more related in meaning.

The basic flow is:

text -> tokens -> token numbers -> embeddings -> neural network

How ChatGPT Works Overall

When you type a message into ChatGPT, your text goes through several steps before you see an answer.

The full flow looks like this:

your input -> tokens -> embeddings -> transformer neural network -> next-token prediction -> final response

1. You write an input

Your message is the prompt. It tells the model what context it should respond to.

Explain machine learning in simple words.

1. The input is split into tokens

The text is broken into smaller pieces called tokens. These tokens may be whole words, parts of words, punctuation, or short character sequences.

Related concept: Token

1. Tokens are converted into numbers

The model cannot directly understand text, so each token is converted into a numerical form.

Related concept: Embedding

1. Embeddings represent meaning

The token numbers are turned into embeddings. Embeddings help the model represent meaning and relationships between words.

For example, words with related meanings can have similar embeddings.

Related concept: Embedding

1. The neural network processes the input

The embeddings move through many neural network layers. Each layer transforms the information and helps the model understand more complex patterns.

Related concepts: Neural Network, Deep Learning

1. Attention finds important context

Inside the Transformer, attention helps the model decide which tokens matter most for the current prediction.

For example, if your prompt says:

Eden has a dog. What pet does Eden have?

Attention helps the model connect “pet” with “dog.”

Related concept: Transformer / Attention

1. The model predicts the next token

ChatGPT does not create the whole answer at once. It predicts one token at a time.

Prompt: Explain machine learning simply.
Next token prediction: Machine
Next token prediction: learning
Next token prediction: is
Next token prediction: ...

Related concept: Machine Learning

1. The answer is built token by token

Each new token becomes part of the context, then the model predicts the next token again. This repeats until the response is complete.

Machine -> Machine learning -> Machine learning is -> Machine learning is a -> ...

1. You see the final response

The predicted tokens are converted back into readable text, and that becomes ChatGPT’s answer.

So the main idea is:

ChatGPT uses machine learning to predict tokens.
Deep learning gives it many layers for learning complex patterns.
A neural network processes the numbers.
Embeddings represent text as meaning-based numbers.
Attention helps it focus on the important parts of your prompt.