Tokenization and Context Windows

How BPE tokenizers actually work, why a 200k-token window is not 200k tokens of usable attention, and how to budget tokens like a scarce resource

A deep, numbers-first look at subword tokenization (BPE), why tokens are not words or characters, the 'lost in the middle' effective-context problem, worked token-budget arithmetic for a real prompt, and why long context windows do not make RAG obsolete. Written as an AI Engineer interview reference with concrete worked examples and comparison tables.

Practice questions (9)

• Sizing a Multi-Turn Support Prompt to a Token Budget

  Intermediate · Free
  View →

• Defending a Cost Estimate Built on Word Count

  Intermediate
  View →

• Diagnosing a 'Lost in the Middle' Failure

  Intermediate
  View →

• A Multilingual Cost Surprise

  Intermediate
  View →

• Pushback: 'Just Use the Big Context Window'

  Intermediate
  View →

Quizzes (1)