🔹 What is Tokenization?

Tokenization is the process of breaking text into smaller pieces called “tokens.”
Why? Because computers don’t naturally understand sentences the way humans do. They need everything in small chunks to process meaning step by step.

🔹 Simple Example

Take this sentence:
👉 “I love apples.”

When we tokenize it, we break it into:

“I”
“love”
“apples”

Now instead of one long sentence, the computer sees 3 small tokens.

🔹 Why do we need it?

Imagine you’re learning a new language. It’s much easier to learn word by word, rather than trying to swallow an entire paragraph at once.

Computers are the same. Tokenization helps by:

Splitting text into smaller parts.
Making it easier for AI to “read” and “understand.”
Handling complex or unknown words by breaking them into familiar pieces.

🔹 Tokens can be Different Sizes

Not all tokens are just words. Depending on the method, tokens can be:

Words – e.g., “I”, “love”, “apples”
Sub-words – e.g., “unhappiness” → “un”, “happi”, “ness”
Characters – e.g., “cat” → “c”, “a”, “t”

This flexibility makes it easier for AI models to deal with new words or unusual spellings.

🔹 Real-Life Analogy

Think of tokenization like cutting a cake into slices:

The whole cake = the sentence.
Each slice = a token.
Smaller slices make it easier to eat and share, just like smaller tokens make text easier for computers to process.

Or, imagine LEGO blocks:

The finished model = the sentence.
Each block = a token.
By breaking it down, you can build, change, or understand the structure better.

🔹 Where Tokenization is Used

You might not realize it, but tokenization happens behind the scenes everywhere:

Search engines (when you type a query, it splits into tokens).
Chatbots & AI assistants (like me—I convert your text into tokens first).
Machine translation (Google Translate breaks text into tokens to translate piece by piece).
Sentiment analysis (analyzing reviews like “good”, “bad”, “excellent”).

🌟 The Bottom Line

Tokenization is just cutting sentences into smaller, meaningful pieces so computers can understand language.
It’s the very first step in most Natural Language Processing (NLP) tasks. Without tokenization, AI wouldn’t be able to “read” at all.

Breaking Text Into Pieces: A Simple Guide to Tokenization