Introduction
Tokenization is one of the most important foundational concepts in Artificial Intelligence (AI) and Natural Language Processing (NLP).
Before an AI model can understand or generate text, it must first break the text into smaller pieces called tokens.
This article explains what tokenization is, what tokens are, and the different types of tokenization used in modern AI systems.
What Is Tokenization?
Tokenization is the process of splitting text into smaller meaningful units so that a computer or AI model can process and understand it.
Example
Sentence:
"AI is transforming the world."
After tokenization:
["AI", "is", "transforming", "the", "world", "."]
These smaller pieces are called tokens.
In simple terms:
Why Is Tokenization Important?
AI models cannot understand raw text directly.
They operate only on numbers.
Tokenization enables this by:
Converting text into tokens and then numeric IDs
Reducing complex language into manageable units
Helping models learn grammar, meaning, and context
Improving processing speed, accuracy, and memory efficiency
Without tokenization, modern AI systems and chatbots would not function.
When Is Tokenization Used?
Tokenization occurs before almost every NLP or AI language task.
Common scenarios include:
Text preprocessing in machine learning pipelines
Training large language models (LLMs)
Sentiment analysis, translation, and summarization
Understanding user input in chatbots
Whenever AI reads or generates text, tokenization happens first.
How Does Tokenization Work?
The process generally follows these steps:
Step-by-Step Flow
Input text
Example: "AI is powerful"
Split text into tokens
["AI", "is", "powerful"]
Convert tokens into numeric IDs
[101, 27, 3056]
Feed numeric data into the AI model
The model learns patterns, meaning, and relationships
Modern AI systems primarily rely on subword tokenization, such as:
Byte Pair Encoding (BPE)
WordPiece
SentencePiece
These methods balance:
What Is a Token in AI?
A token is the basic unit of text that an AI model processes.
Depending on the tokenization method, a token may represent:
AI models do not read full sentences directly. Instead, they convert tokens into numbers and process them mathematically.
Processing Pipeline
Text → Tokens → Numbers → AI Understanding
Key Points About Tokens
More text produces more tokens
More tokens increase processing cost
AI usage cost is calculated per token, not per question
Managing token usage improves performance and cost efficiency
Types of Tokenization
Different tokenization strategies are used based on accuracy, efficiency, and language complexity.
Word Tokenization
Word tokenization splits text into individual words.
Example
Sentence:
"I love AI"
Word tokens:
["I", "love", "AI"]
Advantages
Limitations
Cannot handle new or rare words well
Vocabulary size grows very large
Not suitable for languages without space-separated words
Character Tokenization
Character tokenization breaks text into individual characters.
Example
Sentence:
"AI"
Character tokens:
["A", "I"]
Advantages
Limitations
Subword Tokenization
Subword tokenization splits words into smaller meaningful parts.
Example
Word:
"playing"
Subword tokens:
["play", "ing"]
This is the most widely used approach in modern AI models.
Advantages
Handles rare and unknown words effectively
Maintains a manageable vocabulary size
Preserves semantic meaning better than character-level tokens
Common Subword Techniques
Byte Pair Encoding (BPE)
WordPiece
SentencePiece
Conclusion
Tokenization may appear to be a simple preprocessing step, but it is the foundation of how AI understands language.
Key takeaways:
Tokenization breaks text into smaller units
A token is the unit processed by AI models
The main tokenization types are:
Understanding tokenization is a critical first step toward learning NLP and modern AI systems.