Introduction
Think of a Large Language Model (LLM) like GPT as a next-token prediction machine.
Step 1: Convert Text into Tokens
The model does not read words directly. It first breaks text into tokens.
Example
Input
"What is AI?"
May become:
["What", " is", " AI", "?"]
A token can be:
A word
Part of a word
A punctuation mark
A number
Example
"unbelievable"
might become:
["un", "believ", "able"]
Step 2: Convert Tokens to Numbers
Computers understand numbers, not text.
Each token gets a unique ID:
"What" = 1205
"is" = 451
"AI" = 7231
"?" = 89
Input becomes:
[1205, 451, 7231, 89]
Step 3: Create Embeddings
The model converts token IDs into mathematical vectors.
Example
"What" → [0.23, -0.75, 0.91, ...]
These vectors capture meaning and relationships.
For example:
King ≈ Queen
Dog ≈ Puppy
Car ≈ Vehicle
have similar vector patterns.
Step 4: Use the Attention Mechanism
The Transformer architecture uses Self-Attention.
When processing:
"The cat drank the milk because it was thirsty."
The model learns that:
"it" → refers to "cat"
not "milk".
Attention helps the model focus on important previous tokens.
Step 5: Predict the Next Token
Suppose the input is:
"The capital of France is"
The model calculates probabilities for the next token:
Paris = 95%
London = 2%
Berlin = 1%
Other = 2%
It selects:
Paris
New sequence:
"The capital of France is Paris"
Step 6: Repeat Again and Again
The model now predicts the next token after "Paris".
Probabilities:
"." = 80%
"and" = 10%
"," = 5%
It picks:
"."
Sequence becomes:
"The capital of France is Paris."
This process repeats thousands of times per response.
Example of Token-by-Token Generation
User asks:
What is C#?
The model may generate:
"C#"
then
"is"
then
"a"
then
"modern"
then
"programming"
then
"language"
and so on.
The final response is built one token at a time.
Why Responses Sometimes Differ
The model does not always pick the highest-probability token.
Using a parameter called temperature, it may choose among likely options.
For:
Tell me a joke.
Possible next-token probabilities:
Why = 40%
A = 30%
One = 20%
Different choices produce different responses.
In Simple
LLMs generate responses by first converting text into tokens. These tokens are transformed into numerical representations and processed through a Transformer model using self-attention. The model predicts the most likely next token based on all previous tokens. After generating one token, it adds it to the context and predicts the next token. This process repeats until the complete response is produced, effectively generating text one token at a time.
Summary
Large Language Models generate text by breaking input into tokens, converting those tokens into numerical representations, processing them with a Transformer architecture that uses self-attention, and repeatedly predicting the most probable next token. By generating one token at a time and using the previously generated tokens as context, an LLM can produce coherent and context-aware responses to a wide variety of prompts.