A beginner-friendly guide to tokenization, embeddings, transformers, attention, next-token prediction, and how AI generates text
If you've spent even a little time around Artificial Intelligence, you've probably heard the term LLM everywhere.
ChatGPT, Gemini, Claude, Llama, Mistral and many other modern AI systems are built around large language models.
But there is a question that often gets overlooked:
What actually happens when you type something into an AI chatbot and it gives you an answer?
You type:
"Tell me a story about a teddy bear."
A few seconds later, an entire paragraph appears on your screen.
It feels almost instantaneous.
But underneath that simple interaction is a surprisingly sophisticated pipeline involving tokens, numerical representations, neural networks, attention mechanisms, probability and repeated prediction.
Let's break it down without drowning ourselves in mathematical terminology.
What Is a Large Language Model?
A Large Language Model (LLM) is a machine-learning model trained on enormous amounts of text so that it can learn statistical patterns and relationships in language.
Models such as ChatGPT, Gemini and Claude can generate text, answer questions, summarize documents, write code and perform many other language-related tasks.
But an LLM doesn't work by storing a gigantic collection of ready-made answers and retrieving one whenever you ask a question.
Instead, at a high level, the model processes your input as tokens, transforms those tokens into numerical representations, processes their relationships through a neural-network architecture—typically based on transformers—and predicts what tokens should come next.
A simplified view looks like this:
Your text → Tokenization → Numerical representations → Transformer processing → Next-token prediction → Generated response
And that last part is particularly important.
LLMs generate text one token at a time.
They don't write an entire paragraph in one magical operation.
Let's see what that means.
The Five Pieces Behind an LLM Response
To understand the basic flow, imagine that you type:
🧸 "I like my Teddy Bear."
No, I don't actually own a teddy bear. 😄
We're simply going to use this sentence as our example.
We can roughly divide the process into five stages:
Tokenization
Embeddings
Transformer processing and attention
Next-token prediction
Response generation
Each stage performs a different job.
1. Tokenization: Breaking Text Into Pieces
The first problem an LLM faces is surprisingly simple:
Computers don't receive language in the same form humans do.
You see:
I like my Teddy Bear.
The model needs a numerical representation of that input.
This is where tokenization comes in.
Tokenization converts text into smaller units called tokens.
A token isn't necessarily a complete word.
Depending on the tokenizer, a token can represent:
A complete word
Part of a word
Punctuation
Whitespace-related pieces
Numbers
Other text fragments
For example, our sentence might be divided conceptually into something like:
I|like|my|Teddy|Bear
The actual tokenization depends on the tokenizer being used.
Each resulting token is then associated with a numerical token ID.
So instead of thinking about:
"I like my Teddy Bear"
the model receives something more like:
[token_1, token_2, token_3, token_4, token_5]
The exact numbers aren't important for understanding the concept.
What matters is that human-readable text has now been converted into discrete pieces that the model can process.
Think of tokens as LEGO pieces.
Your sentence is the LEGO structure.
Tokenization breaks that structure into individual pieces.
The model can now work with those pieces mathematically.
Why Doesn't Every Word Equal One Token?
This is an important beginner question.
You might assume:
One word = one token.
That's not necessarily true.
Consider a longer or uncommon word.
A tokenizer may split it into several pieces.
For example, a word could conceptually be represented as:
under+standing
rather than treating the entire word as a single token.
Even spaces and punctuation can influence tokenization.
This is one reason why token count isn't the same thing as word count.
And token count matters because modern LLMs operate within limits such as context windows and token-based pricing.
2. Embeddings: Turning Tokens Into Numbers
Now we have tokens.
But a token ID such as:
10982
doesn't contain enough useful information by itself.
The model needs a richer numerical representation.
This is where embeddings enter the picture.
An embedding is a vector—a list of numbers—that represents a token or piece of information in a mathematical space.
For example, purely for illustration, imagine:
Teddy →
[0.72, -0.45, 1.09, ...]
and:
Bear →
[0.95, -0.33, 1.50, ...]
Real models use much larger vectors, and the exact dimensions depend on the model architecture.
These numbers aren't human-readable definitions such as:
"Teddy = a soft toy."
Instead, they are learned numerical representations that allow the neural network to work with relationships and patterns.
Think of it this way.
Tokenization answers:
"What pieces of text did I receive?"
Embeddings help answer:
"How can I represent those pieces numerically so the neural network can process them?"
That numerical representation is essential because neural networks perform mathematical operations—not dictionary lookups in the way humans might imagine.
3. Transformers and Self-Attention: Understanding Relationships
Now comes one of the most important concepts in modern LLMs:
The Transformer
The Transformer architecture revolutionized natural-language processing because it introduced an efficient way of modeling relationships between tokens using mechanisms such as self-attention.
Let's return to:
"I like my Teddy Bear."
The meaning of one token can depend on the other tokens around it.
For example, "Bear" isn't just an isolated word.
Together:
Teddy + Bear
form a familiar concept.
Similarly, the word "my" provides information about the relationship between the speaker and what follows.
The model needs a mechanism for determining which parts of the input are relevant to one another.
That's where self-attention becomes important.
What Is Self-Attention?
A simple way to think about self-attention is:
Each token can examine other tokens in the sequence and assign different levels of importance to them when building its contextual representation.
Imagine you're sitting in a meeting.
Five people are talking.
Not every statement is equally relevant to every question.
If someone asks:
"Who owns the project?"
you pay more attention to the person discussing ownership than someone talking about the lunch menu.
Self-attention works somewhat similarly at a mathematical level.
For:
"I like my Teddy Bear."
the model can learn relationships between tokens such as:
Teddy ↔ Bear
and
my ↔ Teddy
The important point is that the model isn't simply processing each word in isolation.
It is modeling relationships between tokens.
Attention Is More Than "Looking at Important Words"
You'll often hear explanations like:
"Attention tells the model which words are important."
That's a useful beginner analogy, but technically it's an oversimplification.
Self-attention calculates relationships between token representations using learned transformations and produces context-aware representations.
Modern transformer architectures perform this operation across many layers and attention heads.
Each layer progressively transforms the representations.
That's why the Transformer is much more than a simple keyword-matching system.
It builds increasingly sophisticated representations of the input.
4. Next-Token Prediction: What Comes Next?
Now we reach the core idea behind many generative language models.
Suppose you give the model:
"I like my Teddy"
What comes next?
The model calculates probabilities over possible next tokens.
For example, purely as an illustration:
| Possible token | Example probability |
|---|---|
| Bear | 85% |
| Roosevelt | 5% |
| Dog | 3% |
| Blanket | 1% |
| Other tokens | 6% |
These numbers are fictional examples, not actual probabilities produced by a specific model.
The important idea is that the model assigns probabilities to possible next tokens.
It may then select one according to its decoding strategy.
If:
Bear
is selected, the sequence becomes:
"I like my Teddy Bear"
The model can then predict what comes after that.
This process repeats.
Does an LLM Simply Choose the Highest Probability?
Not always.
This is another common misconception.
A language model produces a probability distribution over possible next tokens.
The system then uses a decoding strategy to determine which token to output.
Depending on the system and settings, techniques can include:
Greedy decoding
Temperature
Top-k sampling
Top-p sampling
Other decoding strategies
This is one reason why asking the same question multiple times can sometimes produce different answers.
The model isn't necessarily following a single deterministic path.
5. Response Generation: One Token at a Time
Now let's put everything together.
You ask:
"Tell me a story about my Teddy Bear."
The model begins generating a sequence.
Conceptually, it might produce:
Once
Then:
Once upon
Then:
Once upon a
Then:
Once upon a time
Then:
Once upon a time there
And so on.
Each newly generated token becomes part of the context used to predict what comes next.
Eventually, you might get:
"Once upon a time, there was a little teddy bear..."
And the process continues until the model reaches an appropriate stopping condition or the generation reaches its configured limit.
So when you watch an AI chatbot generate a response word by word—or more accurately, token by token—that isn't merely a visual trick.
The underlying generation process is sequential.
So Does ChatGPT "Understand" What I Said?
This is where things become philosophically and technically interesting.
We often say:
"The AI understands my question."
That's useful conversational language.
But technically, an LLM processes numerical representations and learned statistical relationships rather than experiencing language in the same way a human does.
It doesn't have a human-like mind sitting behind the screen reading your sentence.
Instead, the model transforms the input through layers of learned parameters and produces a probability distribution over possible outputs.
The result can be remarkably coherent because the model has learned extremely complex patterns from its training process.
This distinction becomes particularly important when we discuss:
Hallucinations
Reasoning
AI agents
Context windows
RAG
Fine-tuning
Model evaluation
These systems become much easier to understand once you stop imagining the LLM as a digital human and start thinking of it as a large neural network performing learned transformations and predictions.
The Complete LLM Pipeline
Let's simplify the entire process.
You type:
"I like my Teddy Bear."
Step 1 — Tokenization
The text is divided into tokens.
↓
Step 2 — Embedding
Those tokens are represented numerically.
↓
Step 3 — Transformer processing
The model processes relationships between tokens using mechanisms including self-attention.
↓
Step 4 — Prediction
The model produces probabilities for possible next tokens.
↓
Step 5 — Generation
A token is selected and appended to the sequence.
↓
Repeat
The model continues predicting the next token until the response is complete.
In simplified form:
Text → Tokens → Vectors → Transformer → Probabilities → Token → Transformer → Probabilities → Token → ... → Response
That's the basic idea behind how a generative LLM produces text.
But There Is Much More to an LLM
The five steps above provide a useful mental model, but they don't represent the entire lifecycle of a modern LLM.
Behind the scenes are much more complex processes involving:
Massive datasets
Pretraining
Optimization
Billions of model parameters
Transformer layers
Attention mechanisms
Positional information
GPUs and distributed computing
Instruction tuning
Human feedback and preference optimization
Safety mechanisms
Inference optimization
Context management
And once you move from a basic LLM to an actual production AI application, another layer appears.
You may need:
LLM + Prompt + Context + Tools + RAG + Memory + Agents + Evaluation
That's where modern Generative AI engineering begins to get particularly interesting.
Why This Understanding Matters
You don't need to become a mathematician to start working with LLMs.
But understanding the fundamentals changes how you use them.
If you understand tokens, you can reason about context windows and token costs.
If you understand embeddings, vector databases and semantic search become easier to understand.
If you understand transformers and attention, concepts such as context and long-range relationships become less mysterious.
If you understand next-token prediction, you begin to understand why an LLM can produce fluent text while still occasionally generating incorrect information.
And if you understand the limitations of an LLM, you can design better systems around it.
That distinction is important.
Using an LLM is one thing.
Engineering systems around an LLM is another.
Final Thoughts
The next time you type something into ChatGPT, Gemini, Claude or another AI assistant, remember what is happening beneath the interface.
Your sentence doesn't simply travel into a machine that "knows" the answer.
It is broken into tokens.
Those tokens are represented numerically.
A transformer processes their relationships.
The model calculates probabilities for possible next tokens.
And the response is generated progressively.
All of that happens incredibly quickly, which makes the experience feel almost instantaneous.
The technology may look magical from the outside.
But once you break it into pieces, the magic starts looking more like engineering.
And that's exactly where the fun begins.
LLMs don't need to be mysterious.
You just need to understand their building blocks.
Frequently Asked Questions
What is an LLM?
A Large Language Model is a neural network trained on large amounts of text to learn patterns and relationships in language and generate text based on an input context.
How does an LLM generate text?
An LLM processes its input as tokens and repeatedly predicts what token should come next until the response is complete.
What is tokenization in AI?
Tokenization converts text into smaller units called tokens that a language model can process.
What are embeddings in an LLM?
Embeddings are numerical vector representations used by neural networks to represent tokens or other pieces of information in a mathematical space.
What is self-attention in a Transformer?
Self-attention is a mechanism that allows the model to calculate relationships between tokens within a sequence and create context-aware representations.
Does ChatGPT generate an entire answer at once?
At a high level, autoregressive language models generate output sequentially, predicting tokens based on the context available at each generation step.
Why are LLMs sometimes wrong if they are good at predicting text?
Because producing a likely sequence of tokens is not equivalent to verifying that every factual statement is true. This is one reason techniques such as retrieval-augmented generation, tool use and external verification can be valuable in AI applications.
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