Tokenization and Context Windows
What are the key trade-offs of Tokenization and Context Windows? When would you choose it vs alternatives?
Answers use simple, clear English.
Audio N/AQuick interview answer
Pros: Open vocabulary; handles rare words via subwords. Cons: Token tax on whitespace/JSON; surprises in cost estimation.
Detailed answer
Pros: Open vocabulary; handles rare words via subwords. Cons: Token tax on whitespace/JSON; surprises in cost estimation. Decision: pick when benefits outweigh operational cost. 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
- How would you test Tokenization and Context Windows?
- What metrics prove Tokenization and Context Windows is healthy in prod?
- How does Tokenization and Context Windows change at 10× traffic?