Tokenization is the process of breaking text into smaller units, called tokens, that language models can understand and process. Tokens can be words, subwords, or characters, serving as the model’s input and output building blocks.
LLMs convert raw text into tokens using predefined rules or algorithms. These tokens map text into numerical representations, enabling the model to analyze and generate language efficiently. Tokenization balances granularity — too small increases length, too large limits flexibility.
Tokenization directly affects model efficiency, response speed, and accuracy. For product managers, optimizing tokenization reduces computational cost, latency, and improves user experience. Proper tokenization ensures scalable and feasible AI products, influencing pricing and deployment strategies.