Tokenization and Context Windows
What common mistakes do candidates make around Tokenization and Context Windows, and how do you avoid them?
Answers use simple, clear English.
Audio N/AQuick interview answer
Common mistakes: Assuming 1 token ≈ 1 word globally. Best practices: Count tokens with the model tokenizer; truncate/summarize for limits.
Detailed answer
Common mistakes: Assuming 1 token ≈ 1 word globally. Best practices: Count tokens with the model tokenizer; truncate/summarize for limits. Core: BPE/WordPiece/Unigram split text into tokens. Cost and context limits are in tokens not words. Multilingual and code tokenize differently. Real-time example: A 4k-character JSON may be 2k–8k tokens depending on tokenizer. Pros: Open vocabulary; handles rare words via subwords. Cons: Token tax on whitespace/JSON; surprises in cost estimation. Common mistakes: Assuming 1 token ≈ 1 word globally. Best practices: Count tokens with the model tokenizer; truncate/summarize for limits. Audience level: Fresher.
Full explanation
BPE/WordPiece/Unigram split text into tokens. Cost and context limits are in tokens not words. Multilingual and code tokenize differently.
Real example & use case
A 4k-character JSON may be 2k–8k tokens depending on tokenizer.
Pros & cons
Pros: Open vocabulary; handles rare words via subwords. Cons: Token tax on whitespace/JSON; surprises in cost estimation.
Common mistakes
Assuming 1 token ≈ 1 word globally.
Best practices
Count tokens with the model tokenizer; truncate/summarize for limits.
Follow-up questions
Open one as its own read / solve / listen card