What Are Tokens in LLMs? Complete Guide to Tokenization

Tokens in LLMs

1. What is a Token?

A token is the basic unit of text that a Large Language Model (LLM) processes.

An LLM does not directly process complete words or sentences. It first breaks the input text into smaller pieces called tokens.

A token can be:

• A complete word
• Part of a word
• A punctuation mark
• A symbol
• Sometimes a space combined with a word

Example

ChatGPT is amazing!

The tokenizer might split it into:

["Chat", "GPT", " is", " amazing", "!"]

The exact tokens depend on the tokenizer used by the model.

2. Why Do LLMs Use Tokens?

Neural networks work with numbers, not raw text.

Therefore, text goes through several steps:

Text
  ↓
Tokenizer
  ↓
Tokens
  ↓
Token IDs
  ↓
Embeddings
  ↓
Transformer
  ↓
Next Token Prediction

For example:

"I love AI"

may become:

["I", " love", " AI"]

Then each token is converted into a numerical ID:

[40, 1842, 921]

The model processes these numerical representations.

3. Tokens Are Not Always Words

A common misconception is:

One token = one word

This is not true.

A single word can contain multiple tokens.

unbelievable

could be split into something like:

["un", "believ", "able"]

On the other hand, a short word may be represented by a single token.

Number of words ≠ Number of tokens

4. Tokenization

Tokenization is the process of converting text into tokens.

Example:

Input:
"I love programming."

Possible tokenization:

["I", " love", " program", "ming", "."]

The component responsible for this process is called a tokenizer.

Different LLMs can use different tokenizers, so the same text may produce different token counts in different models.

5. Common Tokenization Algorithms

BPE — Byte Pair Encoding

BPE learns frequently occurring character or subword patterns and combines them into tokens. It is widely used in modern language models.

WordPiece

WordPiece breaks words into smaller subword units. It was popularized by models such as BERT.

SentencePiece

SentencePiece performs tokenization without requiring traditional whitespace-based word splitting. It is commonly used in various modern multilingual and generative models.

6. Token IDs

After tokenization, each token is mapped to a numerical ID.

Text:
"I love AI"

Tokens:
["I", " love", " AI"]

Token IDs:
[40, 1842, 921]

The exact IDs depend on the model's vocabulary.

The model processes these IDs rather than directly processing the original text.

7. Input Tokens and Output Tokens

LLM applications generally deal with two important types of tokens:

Input Tokens

Tokens that you send to the model.

User Prompt
     ↓
Input Tokens

Output Tokens

Tokens generated by the model.

LLM
 ↓
Output Tokens
 ↓
Response

For example:

Input:  20 tokens
Output: 50 tokens

The model processed 20 input tokens and generated 50 output tokens.

8. Tokens and Context Window

A model has a maximum number of tokens it can process within a single context. This is called the context window.

For example:

Context Window = 128K tokens

This means the model can work with a context containing up to approximately 128,000 tokens, subject to the specific model's limits.

The context can include:

System instructions
        +
Conversation history
        +
User prompt
        +
Other provided information
        +
Generated output

Conceptually:

Context = Input + Available conversation/context + Output

The exact accounting depends on the model and API.

9. Why Token Count Matters

Token count is important for three main reasons.

1. Context Limits

If the context becomes too large, older information may need to be removed or the request may exceed the model's context limit.

2. API Cost

Many LLM APIs calculate pricing based on tokens.

Input tokens  → Input cost
Output tokens → Output cost

Therefore, sending unnecessary text can increase costs.

3. Performance

A larger amount of input generally means the model has more information to process, which can affect latency and resource usage.

10. Tokens in Different Languages

Tokenization differs between languages.

The same amount of text can produce different numbers of tokens depending on:

• Language
• Vocabulary
• Tokenizer
• Writing system
• Model

Therefore:

100 English words

does not necessarily equal:

100 tokens

Similarly, 100 words in another language may result in a different token count.

11. Tokens and Next-Token Prediction

LLMs generate text one token at a time.

For example:

The capital of France is

The model predicts the next token:

Paris

Then it predicts the next token based on the updated sequence.

The capital of France is
                    ↓
                  Paris
                    ↓
                    .

This process continues until the model finishes the response or reaches a generation limit.

12. Complete LLM Token Flow

User Text
    ↓
Tokenizer
    ↓
Tokens
    ↓
Token IDs
    ↓
Embeddings
    ↓
Transformer
    ↓
Next Token Prediction
    ↓
More Tokens
    ↓
Detokenization
    ↓
Human-readable Response

13. Important Terms

Term	Meaning
Token	Basic unit of text processed by an LLM
Tokenizer	Converts text into tokens
Token ID	Numerical identifier representing a token
Vocabulary	Collection of tokens known by a tokenizer or model
Input Tokens	Tokens sent to the model
Output Tokens	Tokens generated by the model
Context Window	Maximum context the model can process
Tokenization	Process of splitting text into tokens
BPE	A subword tokenization technique
WordPiece	A subword tokenization technique
SentencePiece	A tokenizer framework commonly used in language models

Key Takeaways

1. A token is not necessarily a complete word.
2. LLMs process tokens rather than raw text.
3. Tokens are converted into numerical representations before being processed by the neural network.
4. Tokenization converts text into tokens.
5. Input and output tokens are important when working with LLM APIs.
6. Context windows are measured in tokens.
7. Token count affects context limits, cost, and potentially latency.
8. LLMs generate responses by predicting one token at a time.
9. Different models and languages can tokenize the same text differently.
10. Common tokenization approaches include BPE, WordPiece, and SentencePiece.

LLM Tokens - 50 MCQ Quiz

Test your understanding of tokens, tokenization, token IDs, embeddings, context windows, and token usage.

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Questions

Question 1

What is the most accurate definition of a token in an LLM?

1. A complete sentence
2. A complete paragraph
3. A unit of text produced by a tokenizer
4. A database record

Question 2

What is tokenization?

1. Converting tokens back into text
2. Converting text into token units
3. Converting embeddings into text
4. Generating an AI response

Question 3

Is one token always equal to one word?

1. Yes
2. No
3. Only in English
4. Only in large models

Question 4

What is a token ID?

1. The position of a word in a sentence
2. A numerical identifier assigned to a token
3. The probability of a token
4. The token's embedding

Question 5

Why are tokens converted into numerical IDs?

1. Neural networks process numerical data
2. To make text longer
3. To remove context
4. To translate text

Question 6

What does a tokenizer vocabulary contain?

1. Only complete English words
2. Only sentences
3. Token units that the tokenizer recognizes
4. Only punctuation

Question 7

Why can the same sentence have different token counts in different LLMs?

1. Different models can use different tokenizers
2. The sentence changes automatically
3. The GPU changes the text
4. Token counts are random

Question 8

What is BPE?

1. A database
2. A subword tokenization technique
3. A neural network layer
4. An embedding database

Question 9

What is one major advantage of subword tokenization?

1. It guarantees one token per word
2. It balances vocabulary size and sequence length
3. It removes the need for embeddings
4. It removes all unknown words

Question 10

What problem can a purely word-level tokenizer have?

1. It cannot process punctuation
2. Its vocabulary can become extremely large
3. It cannot generate text
4. It cannot create token IDs

Question 11

What problem can character-level tokenization cause?

1. Very long token sequences
2. Infinite vocabulary
3. No numerical representation
4. No punctuation support

Question 12

What is an embedding?

1. A human-readable word
2. A numerical vector representation
3. A tokenizer vocabulary
4. A generated sentence

Question 13

What does an autoregressive LLM primarily predict?

1. The entire response at once
2. The next token
3. The next paragraph
4. The user's next question

Question 14

What happens after an LLM generates a token?

1. The token is discarded
2. It can become part of the context for the next prediction
3. The context is deleted
4. The tokenizer stops permanently

Question 15

What are input tokens?

1. Tokens generated by the model
2. Tokens supplied to the model
3. Tokens stored only in GPU memory
4. Tokens generated after the response

Question 16

What are output tokens?

1. Tokens supplied by the user
2. Tokens generated by the model
3. Vocabulary entries
4. Embedding dimensions

Question 17

Why are input and output tokens important in an LLM API?

1. They determine the programming language
2. They can affect pricing and usage limits
3. They determine the user's password
4. They control the GPU brand

Question 18

What is a context window?

1. A browser window
2. The amount of tokenized context a model can process
3. The tokenizer vocabulary
4. The output screen

Question 19

Which can consume context-window capacity?

1. System instructions
2. Conversation history
3. User prompts and documents
4. All of the above

Question 20

If a model supports a 128K-token context window, what does 128K represent?

1. 128K words
2. 128K characters
3. Approximately 128,000 tokens
4. 128K sentences

Question 21

Why can unnecessary prompt content increase LLM cost?

1. It increases input tokens
2. It changes the user's account
3. It creates more GPUs
4. It increases vocabulary permanently

Question 22

What can happen when the context exceeds the model's limit?

1. The model gains unlimited memory
2. The request may fail or context must be reduced
3. The tokenizer disappears
4. The vocabulary becomes larger

Question 23

Can spaces be part of tokens?

1. Never
2. Yes, depending on the tokenizer
3. Spaces are always separate tokens
4. Spaces are ignored by every tokenizer

Question 24

Why can punctuation affect tokenization?

1. Punctuation can be represented as token units
2. Punctuation is never processed
3. Punctuation becomes a sentence
4. Punctuation changes the GPU

Question 25

Can different languages produce different token counts for similar content?

1. No
2. Yes
3. Only English produces tokens
4. Only Chinese produces tokens

Question 26

What is SentencePiece?

1. A database
2. A tokenization framework
3. A GPU architecture
4. A programming language

Question 27

What is WordPiece?

1. A subword tokenization method
2. An embedding database
3. A context window
4. A transformer layer

Question 28

What is detokenization?

1. Converting generated tokens back into readable text
2. Splitting text into tokens
3. Creating embeddings
4. Deleting context

Question 29

Which represents a simplified LLM text pipeline?

1. Text → Tokenizer → Token IDs → Model → Tokens → Text
2. Text → GPU → Database → Tokenizer
3. Text → CSS → Database → Model
4. Text → Keyboard → GPU → Database

Question 30

Why are subword tokens useful for rare words?

1. Rare words are removed
2. Rare words can be composed from known pieces
3. Rare words become sentences
4. Rare words require no tokenizer

Question 31

If input is 2,000 tokens and output is 500 tokens, what is the combined token usage?

1. 500
2. 1,500
3. 2,000
4. 2,500

Question 32

A context window is 10,000 tokens and input uses 8,000. How many tokens theoretically remain?

1. 1,000
2. 2,000
3. 8,000
4. 10,000

Question 33

Why shouldn't developers blindly send an entire large document to an LLM?

1. It can consume unnecessary tokens and context
2. LLMs cannot read documents
3. Documents cannot contain tokens
4. It permanently changes the model

Question 34

Which technique can reduce unnecessary context?

1. Removing irrelevant information
2. Summarizing information
3. Retrieving only relevant sections
4. All of the above

Question 35

Why is token counting useful in RAG systems?

1. It helps control retrieved context and usage
2. It replaces the vector database
3. It eliminates embeddings
4. It makes retrieval unnecessary

Question 36

What happens if RAG retrieves a lot of irrelevant information?

1. More unnecessary input tokens are sent
2. The tokenizer stops
3. The vocabulary becomes empty
4. Output becomes impossible

Question 37

Why does token optimization matter in production?

1. It can improve cost and context efficiency
2. It changes training data
3. It guarantees perfect answers
4. It removes prompts

Question 38

What is the relationship between token IDs and embeddings?

1. They are exactly identical
2. Token IDs are integers while embeddings are vectors
3. Embeddings are always words
4. Token IDs are generated sentences

Question 39

Why does exact tokenization matter for API usage?

1. Different token counts can change cost
2. It changes the password
3. It changes the programming language
4. It changes the monitor

Question 40

Which is the best mental model for an autoregressive LLM?

1. It treats paragraphs as indivisible objects
2. It processes token representations and predicts subsequent tokens
3. It searches a database for every answer
4. It only understands dictionary words

Question 41

Why is "one token equals one word" a dangerous assumption?

1. It can cause incorrect cost and context estimates
2. It makes training faster
3. It changes the tokenizer
4. It removes embeddings

Question 42

How should a large document be handled when context is limited?

1. Send it repeatedly
2. Retrieve or summarize the relevant information
3. Add random text
4. Add more punctuation

Question 43

What is one important purpose of tokenization?

1. To make text prettier
2. To provide a practical representation of language for numerical processing
3. To replace transformers
4. To guarantee correct answers

Question 44

If a model predicts one token at a time, how does it generate a paragraph?

1. It predicts many tokens sequentially
2. It predicts the entire paragraph as one token
3. It downloads the paragraph
4. It retrieves a fixed paragraph every time

Question 45

Which factor can increase token count?

1. Longer input
2. Repeated instructions
3. Verbose retrieved documents
4. All of the above

Question 46

Why is context management important in an AI application?

1. Models have finite context limits
2. It can reduce unnecessary token usage
3. It can improve relevance
4. All of the above

Question 47

What does an LLM use a token sequence for?

1. To compute representations and predict likely next tokens
2. To store files permanently
3. To replace the operating system
4. To create database schemas

Question 48

Why can token efficiency vary between languages?

1. Tokenizers represent different languages differently
2. Some languages cannot be tokenized
3. Only English has tokens
4. Languages have different GPUs

Question 49

Which statement about tokens is correct?

1. Tokens can only be complete words
2. Tokens can represent parts of words, punctuation, and other text units
3. Tokens are always characters
4. Tokens are always sentences

Question 50

Why should an AI engineer understand tokens?

1. To understand context limits
2. To estimate API usage and cost
3. To optimize prompts and RAG systems
4. All of the above

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Answer Key

1. C — A unit of text produced by a tokenizer
2. B — Converting text into token units
3. B — No
4. B — A numerical identifier assigned to a token
5. A — Neural networks process numerical data
6. C — Token units that the tokenizer recognizes
7. A — Different models can use different tokenizers
8. B — A subword tokenization technique
9. B — It balances vocabulary size and sequence length
10. B — Its vocabulary can become extremely large
11. A — Very long token sequences
12. B — A numerical vector representation
13. B — The next token
14. B — It can become part of the context for the next prediction
15. B — Tokens supplied to the model
16. B — Tokens generated by the model
17. B — They can affect pricing and usage limits
18. B — The amount of tokenized context a model can process
19. D — All of the above
20. C — Approximately 128,000 tokens
21. A — It increases input tokens
22. B — The request may fail or context must be reduced
23. B — Yes, depending on the tokenizer
24. A — Punctuation can be represented as token units
25. B — Yes
26. B — A tokenization framework
27. A — A subword tokenization method
28. A — Converting generated tokens back into readable text
29. A — Text → Tokenizer → Token IDs → Model → Tokens → Text
30. B — Rare words can be composed from known pieces
31. D — 2,500
32. B — 2,000
33. A — It can consume unnecessary tokens and context
34. D — All of the above
35. A — It helps control retrieved context and usage
36. A — More unnecessary input tokens are sent
37. A — It can improve cost and context efficiency
38. B — Token IDs are integers while embeddings are vectors
39. A — Different token counts can change cost
40. B — It processes token representations and predicts subsequent tokens
41. A — It can cause incorrect cost and context estimates
42. B — Retrieve or summarize the relevant information
43. B — To provide a practical representation of language for numerical processing
44. A — It predicts many tokens sequentially
45. D — All of the above
46. D — All of the above
47. A — To compute representations and predict likely next tokens
48. A — Tokenizers represent different languages differently
49. B — Tokens can represent parts of words, punctuation, and other text units
50. D — All of the above