// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Tokenization
The process of breaking down a piece of text into smaller units, called "tokens," which are then used by an AI model to understand and process the language.
TECHNICAL DEFINITION
Tokenization is the process of converting raw text into discrete numerical units, or tokens, which are the fundamental input and output elements for large language models (LLMs), typically involving subword units (e.g., BPE, WordPiece) to handle vocabulary size and out-of-vocabulary words.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Text segmentation
- Word breaking
- Subword encoding
- Lexical analysis
USAGE NOTE
Understanding tokenization is essential for managing context length and predicting LLM costs.
DEVELOPERS
Organizations developing technology related to Tokenization.
A leading provider of open-source tools and models for natural language processing, including the Transformers library, which offers a wide range of pre-trained tokenizers and tokenizer architectures essential for AI engineering and prompt design.
Creators of the GPT series of large language models, OpenAI develops and employs sophisticated tokenization schemes (like tiktoken for BPE variants) that define how their models process and understand input text for prompt engineering.
Google's AI divisions develop and research various tokenization methods, including SentencePiece and WordPiece, which are foundational to models like BERT, T5, and Gemini, directly impacting how prompts are processed and interpreted.
Meta AI conducts extensive research in NLP and develops large language models like Llama, which utilize specific tokenization strategies to encode text effectively for model training and inference, crucial for prompt performance.
Microsoft's AI research and cloud services integrate and advance tokenization techniques within their NLP offerings and foundational models, supporting efficient text processing for various AI applications and prompt engineering tasks.
Developers of the Claude family of AI models, Anthropic employs robust tokenization methods to manage the input and output processing of their models, directly influencing prompt design effectiveness and model understanding.
Cohere builds foundational large language models for enterprise applications, with tokenization being a core component of their NLP pipeline to efficiently encode textual data for their models, impacting how users design and interact with prompts.
SpaCy is an industrial-strength natural language processing library that includes highly optimized and customizable tokenization capabilities, essential for preparing text data for AI models and understanding linguistic structures relevant to prompt design.