How LLMs Generate Text: Tokens to Responses

When you type a question into an LLM, you see a simple process:
Your prompt → An answer
But inside the model, the process is much more complex.

A simplified view looks like this:

Text
↓
Tokenization
↓
Embeddings
↓
Transformer
↓
Attention
↓
Next-token prediction
↓
Repeated generation
↓
Response

The key idea is simple: an LLM does not generate an entire answer in one step. It processes the input, builds numerical representations of the text, uses the Transformer architecture to process the context, and then generates tokens step by step.

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1. Tokenization: Turning Text Into Tokens​

The first step is tokenization.
An LLM does not directly process sentences as humans do. Instead, the input text is split into smaller units called tokens.
A token can represent:

• A complete word
• Part of a word
• Punctuation
• Spaces or other text patterns, depending on the tokenizer

For example, a sentence such as:

Learning Python is useful.

may be divided into several tokens rather than being treated as one complete sentence.
The exact tokens depend on the tokenizer and vocabulary used by the model.

So the first transformation is roughly:

Text → Tokens

This allows the model to work with a numerical representation of the input.

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2. Embeddings: Turning Tokens Into Numbers​

Tokens are still discrete symbols. Neural networks need numerical data to process them.
This is where embeddings come in.
Each token is mapped to a vector of numbers that represents information the model can use during computation.

A simplified view is:

Token → Vector

These vectors exist in a high-dimensional space. During model training, the network learns representations that help it process relationships and patterns in language.
For example, words or tokens that frequently appear in related contexts can develop useful relationships in the model's learned representation.
However, embeddings are only one part of the process. The model still needs to process the entire context and determine how different parts of that context interact.
That is where the Transformer becomes important.

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3. The Transformer Processes the Context​

Modern LLMs are built around the Transformer architecture.
Instead of simply processing each token independently, Transformer layers repeatedly transform the representations of the tokens using mechanisms such as attention and feed-forward neural network layers.
The goal is to produce increasingly useful representations of the context.

For example, consider:

The developer opened the terminal because it was needed to run the script.

Understanding what it refers to requires information from other parts of the sentence.
The Transformer provides the architecture needed to model these kinds of relationships across the context.

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4. Attention Helps Connect Relevant Information​

One of the most important components of the Transformer is attention.
Attention allows the model to calculate how strongly different token representations should interact with one another.
In simple terms, the model can use attention to determine which parts of the available context are more relevant when processing a particular token.

For example:

Sarah put the laptop in the bag because it was heavy.

The relationship between it and the surrounding context requires the model to consider information from other tokens.
Attention helps the network build contextual representations by allowing information from different positions to interact.

💡 A useful way to think about it is:
Tokenization breaks the text into manageable pieces.
Embeddings represent those pieces numerically.
Attention helps the model process relationships between those pieces.
Transformer layers repeatedly transform these representations to produce information useful for the next stage.

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5. Next-Token Prediction​

After processing the context, the model produces a probability distribution over possible next tokens.
This is the core idea behind autoregressive text generation.
Suppose the input ends with: Python is a
The model may assign different probabilities to possible next tokens, such as programming, language, or other possibilities.
The actual generation process depends on the model and its decoding settings.
The important point is that the model selects or samples a next token based on the probabilities it produces.
Then the new token becomes part of the context for the next prediction.

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6. Generation Happens Token by Token​

This is one of the most important concepts to understand about LLMs.
The model does not normally generate the complete response internally as one giant block.

Instead, generation proceeds step by step:

Input context
↓
Predict next token
↓
Add the token to the context
↓
Predict the next token
↓
Add the token to the context
↓
Continue...

For example:

The
→ The model
→ The model can
→ The model can generate
→ The model can generate text
→ ...

Each new token can influence what comes next.
This repeated process is often called autoregressive generation.

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7. The Complete Process​

Putting the main concepts together, we get:

User input
↓
Tokenization
↓
Token IDs
↓
Embedding and positional information
↓
Transformer layers
↓
Attention + other neural network operations
↓
Output logits
↓
Probability distribution
↓
Next-token selection
↓
New token added to the context
↓
Repeat
↓
Generated response

This is a simplified representation. Real LLM architectures can include additional components and implementation details, but this flow captures the basic idea behind autoregressive text generation.

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Why This Matters​

Understanding this pipeline makes LLMs much easier to reason about.
An LLM is not simply taking a question, "understanding" it in the same way a human does, and then retrieving a prewritten answer.
Instead, the model performs a large number of numerical computations over token representations and context, then generates text one token at a time.
That also explains why the same prompt can sometimes produce different responses. The generation process can involve probabilistic token selection and decoding strategies.
It also explains why context matters so much.
The tokens available to the model can influence the representations and probabilities used during generation.

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A Simple Mental Model 🧠​

If you want a compact mental model for how an LLM works, remember this sequence:

Text
↓
Tokens
↓
Numerical representations
↓
Transformer processing
↓
Contextual relationships
↓
Next-token probabilities
↓
Token generation
↓
Response

You do not need to understand every mathematical detail to start learning how LLMs work.

But if you want to go deeper, these concepts are a good foundation:

• Tokenization
• Embeddings
• Positional information
• Transformers
• Attention
• Logits and probabilities
• Autoregressive generation
• Decoding strategies

💡 The big idea: An LLM turns text into numerical representations, processes relationships within the context through Transformer layers, predicts what token should come next, and repeats that process until the response is generated.
Once you understand that pipeline, topics like self-attention, transformer layers, context windows, and decoding become much easier to understand.

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Frequently Asked Questions​

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Does an LLM generate the whole answer at once?​

No. In autoregressive generation, the model generates the response sequentially, predicting one token at a time and using the generated tokens as part of the context for subsequent predictions.

What is a token in an LLM?​

A token is a unit of text processed by a language model. Depending on the tokenizer, it can represent a word, part of a word, punctuation, or another text fragment.

What is the role of attention in an LLM?​

Attention allows the model to process relationships between different token representations and determine how information from the context should interact when building contextual representations.

Are embeddings the same as the model's understanding?​

Not exactly. Embeddings are numerical representations used by the neural network. The model's behavior results from many layers of learned transformations and computations, not from embeddings alone.