Your question is Tokenization in LLM Performance. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
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?