When you interact with ChatGPT, Bard, Claude, or any Generative AI tool, your text isn’t directly understood as letters or words.
Instead, AI models see your input as numbers.
The process of turning your text into these numbers is called Tokenization.

Think of it like this:

You’re translating a book into a secret code only the AI can read.

1. What is a Token?

A token is like a small piece of your text — it can be:

• A full word (cat)

• Part of a word (play and ing)

• Even punctuation (., ,, ?)

The AI doesn’t see “I love playing football” as one sentence.
It might break it into tokens like:

["I", " love", " play", "ing", " football"]

Each of these tokens gets mapped to a number in the AI’s dictionary (called a vocabulary).

2. Why Do We Need Tokenization?

Computers can’t “read” letters. They only process numbers.
Tokenization:

1. Converts text → tokens → numbers

2. Lets AI models handle different languages, spelling variations, and punctuation

3. Makes training faster and more memory-efficient

Without tokenization, the AI would need to memorize every possible word, which is impossible.

3. How Tokenization Works in Generative AI

Imagine we have the sentence:

Artificial intelligence is amazing!

The AI will:

1. Split the text into tokens using rules from its tokenizer.

2. Look up each token in its vocabulary table.

3. Replace tokens with numbers (IDs).

4. Feed these numbers into the neural network for processing.

["Artificial", " intelligence", " is", " amazing", "!"]
→ [1123, 4578, 33, 990, 2]

4. Tokens and Costs in AI Tools

Most AI APIs (like OpenAI or Anthropic) charge based on tokens, not words.
This includes:

• Tokens in your prompt (input to the AI)

• Tokens in the response (output from the AI)

💡 1 token ≈ 4 characters in English, or ~¾ of a word.
For example, “ChatGPT is cool” is ~4 tokens.

5. Types of Tokenization

There are several methods to break text into tokens:

• Word-level — Each word is a token (simple but can’t handle unknown words well).

• Character-level — Each letter is a token (good for some languages, but inefficient for English).

• Subword-level — Break words into smaller chunks like play + ing (most modern AI models use this, e.g., Byte Pair Encoding / BPE).

6. Tokenization in Action: GenAI Example

Let’s say you type:

Write a poem about the moon.

Behind the scenes:

1. Your sentence is tokenized into small chunks.

2. AI turns each chunk into numbers.

3. The model predicts the next token over and over until it finishes your poem.

4. Numbers are converted back into readable text.

7. Why Freshers Should Care

Understanding tokenization helps you:

• Estimate costs when using AI APIs.

• Optimize prompts to fit within token limits.

• Debug weird outputs when AI “breaks” a word.

• Build custom AI applications that interact with tokenizers.

Try Tokenization Yourself!

Understanding tokenization is great, but seeing it in action makes it even more fun.
I’ve built a simple Tokenization Visualizer App where you can type any text and instantly see how it’s broken down into tokens and converted into numbers just like Generative AI models do behind the scenes.

https://tokenizer-git-master-ankits-projects-36e4fcc1.vercel.app/

Final Thoughts

Tokenization is like learning the alphabet before you write a story.
It’s a small step, but without it, Generative AI wouldn’t understand us at all.

Next time you use ChatGPT, remember:
You’re not just typing words — you’re creating a secret code the AI is decoding and expanding into something amazing.