NLP
Tokenization
The process of breaking text into small pieces (tokens) that an AI can process, like splitting a sentence into words and word-parts.
Definition
The process of converting raw text into a sequence of tokens for model processing. Different tokenizers (BPE, WordPiece, SentencePiece) produce different token sequences. Affects model performance and cost.
Why it matters
Determines how efficiently a model processes text, bad tokenization wastes context window space and increases costs.
Related terms in NLP
BERT
Google's breakthrough AI model that reads sentences in both directions at once to understand context better.
Chain of Thought (CoT)
Asking an AI to "show its work" and think step-by-step, which makes it much better at solving math and logic problems.
Context Window
The maximum amount of text an AI can read and consider at one time, like how many pages of notes it can hold in its head.
Conversational AI
AI that can have natural back-and-forth conversations with humans, chatbots, voice assistants, and customer service bots.
From vocabulary to outcomes
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