From Words to Tokens: Understanding How Large Language Models Think
Every day, millions of people ask ChatGPT questions like:
"Write me a React application."
"Explain recursion like I'm five."
"Help me prepare for my interview."
Within a few seconds, ChatGPT responds with what often feels like a thoughtful answer.
But have you ever wondered:
How does ChatGPT actually understand your question?
Does it search Google?
Does it have every webpage memorized?
Does it understand English the way humans do?
The answer is fascinating.
Behind every response is a series of mathematical operations that convert your words into numbers, analyze relationships between those numbers, and predict what should come next.
At the center of this process are three important ideas:
- Large Language Models (LLMs)
- Tokenization
- Transformers
In this article, we'll explore how ChatGPT processes your questions—from the moment you press Enter until you receive a response.
You don't need any background in AI or machine learning. We'll keep everything beginner-friendly and focus on understanding the concepts rather than complex mathematics.
What is an LLM?
LLM stands for:
Large Language Model
Let's understand each word.
Large
These models are trained using enormous amounts of text from books, articles, documentation, websites, research papers, and publicly available sources.
The word large also refers to the enormous number of parameters (internal values learned during training) that help the model recognize patterns in language.
Language
The model works with human language.
Examples include:
- English
- Hindi
- Spanish
- Japanese
- Programming languages like JavaScript and Python
Language is simply the information the model learns to process.
Model
A model is a mathematical system that has learned patterns from training data.
Instead of memorizing exact answers, it learns relationships such as:
- Which words commonly appear together
- How sentences are structured
- How ideas connect
- How conversations usually flow
This allows the model to generate new responses instead of repeating stored ones.
What Problems Do LLMs Solve?
LLMs are designed to understand and generate human language.
They help with tasks like:
- Answering questions
- Writing emails
- Summarizing documents
- Translating languages
- Explaining concepts
- Generating code
- Fixing bugs
- Brainstorming ideas
Instead of building separate AI systems for every language task, one large model can perform many of them.
Popular Examples of LLMs
Today, several organizations have built powerful LLMs.
Some well-known examples include:
- ChatGPT
- Gemini
- Claude
- Llama
- Mistral
- DeepSeek
Although they differ in training methods and capabilities, most modern LLMs are based on the same fundamental Transformer architecture.
Common Applications in Daily Life
Even if you don't realize it, you've probably already interacted with LLMs.
Examples include:
- AI chatbots
- Coding assistants
- Smart email writing
- Customer support bots
- AI search engines
- Document summarization
- Language translation
- Meeting note generation
As AI becomes more common, LLMs are becoming part of everyday software.
What Happens When You Send a Message to ChatGPT?
Let's imagine you type:
Explain React Hooks simply.
What happens next?
Many people think ChatGPT searches the internet and copies an answer.
That isn't how it normally works.
Instead, several processing steps occur.
Step 1: You Type a Prompt
Everything begins with your prompt.
Example:
Explain React Hooks simply.
The prompt becomes the input for the model.
Step 2: Your Message Is Processed
The model cannot work directly with human-readable text.
Before any reasoning begins, your message is prepared for the AI system.
This involves:
- Breaking text into smaller pieces
- Converting those pieces into numbers
- Sending those numbers into the neural network
We'll soon see why this conversion is necessary.
Step 3: The Model Generates a Response
The model predicts the most appropriate next token repeatedly until the response is complete.
It doesn't generate the entire paragraph at once.
Instead, it predicts:
First token
↓
Second token
↓
Third token
↓
...
↓
Final response
One token at a time.
Does ChatGPT Copy Answers From the Internet?
A common misconception is that ChatGPT searches websites and copies information.
Generally, that's not how it works.
During normal conversations, the model generates responses based on patterns it learned during training.
It predicts likely continuations rather than retrieving and copying documents.
Some AI systems can also use external tools such as web search, but language generation itself is still handled by the model.
Why Computers Don't Understand Human Language
Humans naturally understand words.
Computers do not.
For a computer:
Hello
is not meaningful.
Neither is:
JavaScript
or
React
A computer only understands numbers.
Everything—including text, images, audio, and video—must eventually be represented numerically.
Text vs Numbers
Imagine you ask:
What is React?
Humans instantly recognize:
- "What"
- "is"
- "React"
A computer sees only characters.
Before it can process meaning, the text must become numbers.
This conversion is one of the first steps inside every LLM.
Introducing Tokens
You might think each word becomes one number.
Sometimes that's true.
But often it isn't.
Instead, language models break text into tokens.
A token is a small unit of text.
Depending on the word, a token might represent:
- A whole word
- Part of a word
- A punctuation mark
- A number
- A special symbol
Tokens are the actual language units used by LLMs.
Why Tokenization Is Needed
Languages contain millions of unique words.
People also invent new words every day.
Rather than storing every possible word, tokenization breaks text into reusable pieces.
This makes processing much more efficient.
It also allows the model to understand unfamiliar words by combining smaller parts.
What Is Tokenization?
Tokenization is the process of splitting text into tokens.
Example:
Input:
I love programming.
Possible tokens:
[I]
[love]
[programming]
[.]
Each token is then converted into a numeric ID.
Words vs Tokens
One word is not always one token.
Example:
JavaScript
might become:
Java
Script
A longer word might be divided into several smaller pieces.
Even punctuation becomes tokens.
For example:
Hello!
may become:
Hello
!
This flexibility helps models efficiently process many different languages and writing styles.
A Simple Tokenization Example
Suppose you type:
I love AI.
The processing pipeline looks like this:
Input Text
↓
Tokens
↓
Numeric IDs
↓
AI Model
Notice that the model never directly works with the original sentence.
It works with numbers representing those tokens.
Why Tokens Matter
Every LLM has a context window, which limits how many tokens it can consider at once.
This is why prompts have length limits.
Long conversations, documents, and code are all measured in tokens rather than characters or words.
Understanding tokens helps explain:
- Why prompts have limits
- Why responses can stop
- Why very long conversations eventually lose earlier context
Transformers: The Technology That Changed AI Forever
So far we've learned that ChatGPT doesn't read words directly.
Instead, your message goes through this process:
Your Message
↓
Tokenization
↓
Numbers
But what happens after the text becomes numbers?
This is where the real intelligence begins.
The technology responsible for this is called the Transformer.
If Large Language Models are the brain of modern AI, then the Transformer is the engine that makes that brain work.
Almost every major language model today—including ChatGPT, Gemini, Claude, Llama, Mistral, and DeepSeek—is built on the Transformer architecture.
What is a Transformer?
A Transformer is a deep learning architecture introduced in 2017 by researchers at Google in the famous paper:
"Attention Is All You Need."
Before Transformers, AI models struggled with understanding long sentences and maintaining context.
Transformers solved this problem by allowing models to understand relationships between words, regardless of how far apart they appear in a sentence.
Instead of reading text one word at a time, Transformers analyze the entire sequence together.
This was a major breakthrough in Natural Language Processing (NLP).
Why Transformers Changed AI
Earlier language models had several limitations:
- They struggled with long paragraphs.
- They often forgot information mentioned earlier.
- Training was slower.
- Parallel processing was difficult.
Transformers introduced a new way of processing language that made training much faster and improved understanding of context.
This single innovation enabled the creation of today's powerful LLMs.
Understanding Context
Let's look at a simple sentence:
The trophy didn't fit into the suitcase because it was too big.
What does "it" refer to?
- The trophy?
- The suitcase?
As humans, we understand that "it" refers to the trophy.
Why?
Because we understand the relationship between the words.
A Transformer tries to learn these relationships statistically during training.
Instead of looking at one word in isolation, it considers the surrounding context.
Thinking About Relationships
Imagine reading this sentence:
Shivam is learning React because he wants to build mobile apps.
When the model reaches the word "he", it needs to know who "he" refers to.
A Transformer learns that "he" is closely related to "Shivam."
Similarly, when it encounters "React," it recognizes that "mobile apps" is related to technologies often used in application development.
The model builds these relationships across the entire sentence.
The Idea of Attention
One of the biggest innovations in Transformers is something called Attention.
Think about how humans read.
When reading a sentence, we don't give every word equal importance.
Some words matter more than others.
Example:
The cat chased the mouse.
If someone asks:
Who chased the mouse?
You immediately focus on:
- cat
- chased
- mouse
You ignore less important words.
Transformers try to do something similar.
They learn which words should receive more attention when predicting the next token.
This is why the architecture is called a Transformer based on the attention mechanism.
A Simple Analogy
Imagine you're reading a mystery novel.
On page 200, the author mentions a clue introduced on page 20.
You remember that earlier clue because it is important.
Similarly, a Transformer can learn that a word appearing much earlier in the input is still relevant to understanding the current word.
This ability to connect distant pieces of information is one of the reasons Transformers perform so well.
From Tokens to Understanding
Once your message has been tokenized, the Transformer processes all the tokens together.
At a high level, the workflow looks like this:
Input Text
↓
Tokenization
↓
Tokens
↓
Transformer
↓
Next Token Prediction
↓
Response
The Transformer doesn't "understand" language the way humans do.
Instead, it learns incredibly complex statistical patterns between tokens.
Predicting the Next Token
One of the most surprising facts about ChatGPT is that it doesn't generate an entire answer all at once.
Instead, it repeatedly asks itself:
"Given everything I've seen so far, what is the most likely next token?"
Suppose your prompt is:
React is a JavaScript...
The model predicts something like:
library
Now the sentence becomes:
React is a JavaScript library...
Next prediction:
for
Then:
building
Then:
user
Then:
interfaces
This process continues one token at a time until the response is complete.
What is a Context Window?
While generating a response, the model doesn't remember every conversation you've ever had.
Instead, it only considers a limited amount of recent information.
This limit is called the context window.
Think of it as the model's working memory.
Example:
Conversation
Message 1
Message 2
Message 3
Message 4
Message 5
──────────────
Context Window
──────────────
Everything inside the context window can influence the next response.
Older information may eventually fall outside this window if the conversation becomes very long.
Why Context Matters
Consider these two prompts.
Prompt 1:
Explain JavaScript.
Prompt 2:
Explain it again, but this time for beginners.
The second prompt only makes sense if the model remembers the first one.
Because both prompts are inside the context window, the model can connect them.
This is why conversations feel natural.
Temperature: Why AI Can Give Different Answers
Another interesting concept is temperature.
Temperature controls how predictable or creative the generated text is.
Think of it as a creativity dial.
Low Temperature
A low temperature makes the model choose the most likely next token.
Example:
Prompt:
What is HTML?
Possible response:
HTML is the standard markup language used to create web pages.
If you ask again, the answer will probably be very similar.
Low temperatures produce more consistent and deterministic outputs.
High Temperature
A higher temperature allows the model to consider less likely tokens.
Prompt:
Write a story about a robot.
Possible responses may vary significantly each time.
One response might be humorous.
Another might be emotional.
Another could become a science-fiction adventure.
Higher temperatures increase creativity but can also make responses less predictable.
Complete High-Level Workflow
Let's put everything together.
When you ask ChatGPT a question, this is what happens at a high level:
You Type a Prompt
↓
Text is Tokenized
↓
Tokens Become Numbers
↓
Transformer Processes Relationships
↓
Model Predicts Next Token
↓
Another Token
↓
Another Token
↓
Complete Response Generated
Every response follows this same overall process.
Why ChatGPT Feels Intelligent
ChatGPT often feels like it's reasoning the way humans do.
In reality, its responses come from learning patterns across enormous amounts of text during training.
It has learned:
- Grammar
- Sentence structure
- Programming languages
- Writing styles
- Question-answer patterns
- Logical relationships
- Problem-solving patterns
Using those learned patterns, it predicts the next token repeatedly to construct a response.
This combination of large-scale training, tokenization, and Transformers is what creates the conversational experience we see today.
Key Takeaways
Let's quickly recap everything we've learned.
Large Language Models (LLMs)
LLMs are AI models trained to understand and generate human language.
They solve tasks such as answering questions, writing code, summarizing text, translating languages, and much more.
Tokenization
Computers cannot directly understand words.
They first split text into smaller units called tokens, then convert those tokens into numbers.
Transformers
Transformers analyze relationships between tokens using attention mechanisms.
This allows models to understand context and generate coherent responses.
Context Window
The context window represents the information currently available to the model while generating a response.
Longer context windows allow the model to remember more of the ongoing conversation.
Token Prediction
ChatGPT generates responses one token at a time.
Each new token is predicted based on all the tokens that came before it.
Final Thoughts
When you press Enter after typing a question, it may seem like ChatGPT instantly understands what you mean.
Behind the scenes, however, an incredible sequence of steps takes place.
Your words become tokens.
Tokens become numbers.
Those numbers flow through a Transformer model that analyzes relationships, context, and patterns learned during training.
Finally, the model predicts one token after another until a complete response appears on your screen.
Understanding this process doesn't just help you appreciate modern AI—it also helps you become a better prompt writer.
The better you understand how Large Language Models process information, the better you'll be able to communicate with them and unlock their full potential.
The next time ChatGPT answers your question, you'll know that it isn't searching the internet or copying text.
It's performing billions of mathematical operations to predict the most appropriate next token—one small piece at a time.
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