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21 of 30Tokenization
Concept in One Line
Tokenization is the process of breaking text into tokens so that GPT can convert them into numbers and process language.
Why This Concept Is Important
- Explains how GPT reads text.
- Helps understand token limits and costs.
- Important for interviews and prompt design.
Key Points to Remember
- Tokenization splits text into small units.
- Tokens can be words, parts of words, or symbols.
- Different models use different tokenizers.
- The same sentence can have different tokens.
- Tokenization happens before processing.
Simple Tokenization Example
Sentence: ChatGPT is powerful
Possible tokens:
- Chat
- GPT
- is
- powerful
Tokens depend on the tokenizer.
Why Tokenization Is Needed
- Computers work with numbers, not text.
- Tokenization makes text machine-readable.
- Smaller pieces improve prediction accuracy.
Simple Flow
Raw Text -> Tokenization -> Tokens -> Numbers -> Model
Interview / Exam Points
Q1: What is tokenization?
Tokenization is the process of converting text into tokens for model processing.
Q2: Is tokenization same for all models?
No. Different models use different tokenization methods.
Common Confusions to Avoid
- Tokenization is not word splitting only.
- Tokens are not characters.
- Tokenization happens automatically.
- Token count affects cost and limits.
One Line to Remember
Tokenization converts human text into machine-friendly tokens.
Quick Self-Check
For Students
Tokenization converts text into tokens - Yes / No?
Tokenization depends on the model - Yes / No?
(Correct answers: Yes, Yes)
🧠 Test Your Knowledge
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