You are working with a large language model and need to understand how text is split before it reaches the model. Different tokenization schemes can change sequence length, vocabulary coverage, and how much context fits into the model window.
How does tokenization affect the performance and context handling of an LLM?
How subword tokenization changes sequence lengthWhy tokenizer choice affects embeddings and model behaviorHow token counts impact context-window usage and cost