Tokenization
TechnicalWhat It Means
Tokenization is how AI systems break down text into digestible pieces - like words, parts of words, or characters - before processing them. Think of it as the AI's way of 'reading' text by chopping it into bite-sized chunks it can understand and work with.
Why Chief AI Officers Care
Different tokenization approaches directly impact AI model performance, costs, and capabilities across languages and domains. Poor tokenization choices can lead to biased outputs, increased computational costs, or models that struggle with industry-specific terminology, affecting both ROI and risk management.
Real-World Example
A customer service chatbot trained with basic tokenization might struggle with technical product names or abbreviations, breaking 'iPhone14Pro' into confusing fragments, while better tokenization would recognize it as a single meaningful product identifier.
Common Confusion
Many executives assume tokenization is just 'splitting text by spaces,' but modern AI uses sophisticated methods that can break single words into multiple tokens or combine multiple words into one token based on statistical patterns.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI applications, tokenization must carefully handle medical terminology, patient identifiers, and clinical...
Finance: In finance, tokenization refers to replacing sensitive financial data like credit card numbers or account information wi...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
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Technical Definitions
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