If you’re starting out in AI, Natural Language Processing (NLP), or just curious about how tools like ChatGPT understand text, you’ll run into a strange but important word: tokenization.
It might sound like some secret coding spell but really, it’s just the AI version of chopping vegetables before cooking. Let’s break it down.
What is Tokenization?
Tokenization is the process of splitting text into smaller, meaningful pieces called tokens.
These tokens can be:
Whole words
Parts of words (sub-words)
Even punctuation marks or symbols
The point? AI can’t work directly with raw sentences. Tokens are the prepped ingredients that models actually understand.
Why Do We Need Tokenization?
Computers don’t “read” like humans. They understand numbers.
Tokenization is the bridge:
Break text into tokens.
Assign each token a unique ID (a number).
Feed those IDs into the model.
Without tokenization, a sentence like:
I love coding
is just a messy blob of characters to a computer.
Quick Example
Text:
I love coding.
Tokens:
["I", "love", "coding", "."]
Token IDs (example):
[1, 25, 302, 7]
The exact numbers depend on the tokenizer you’re using each one has its own dictionary (also called a vocabulary).
Subword Tokenization (Used in GPT)
Large models like GPT often split rare words into subwords for flexibility.
Example:
Unbelievable → ["Un", "believ", "able"]
This way, even if the model has never seen “unbelievable” as one word, it still understands it by piecing together familiar chunks.
Why It Matters in AI
Token limits: Models have a maximum number of tokens they can process. More tokens = higher cost and slower response.
Efficiency: Breaking words into subwords lets models handle rare or made-up words without storing them all in the dictionary.
Try It Yourself
You can experiment with tokenization right now using my Custom Tokenizer Tool:
🔗 https://custom-tokenizer-rith.vercel.app/
Type any text and see how it’s split into tokens and turned into IDs just like AI does before generating a response.
A Fun Analogy
Imagine you’re explaining your favorite recipe to a robot chef:
First, you chop the ingredients (tokenization).
Then, you label each ingredient (token IDs).
Finally, the robot uses those labels to cook (model processing).
No chopping = no cooking. That’s how important tokenization is in AI.
Key Takeaways
Tokenization = Text → Tokens → Numbers → Model
It’s step one in almost every NLP task.
Different tokenizers split text differently.
Subwords = flexibility, but more tokens to process.
Summary
Tokenization is how AI breaks down human language into bite-sized chunks it can understand and process.
It’s the translation step between words and the numerical world of machine learning. Whether splitting into whole words, subwords, or even punctuation, tokenization makes it possible for models to handle any text from everyday phrases to rare, complex terms. Without it, AI would be staring blankly at a wall of letters.
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