AICL 103 — Information Theory & Tokenization

Understand how language models turn technical language into tokens, why segmentation affects context budgets and engineering vocabulary, and how BPE-style tokenization works from first principles.

AI Cloud tutorial provenance: AICL 103. Delivered through the AI Academy at Northline Technology Institute as a professional tutorial.

Inside the course: information entropy and compression intuition; byte and subword tokenization; BPE merge logic; technical-vocabulary segmentation; context-window budgeting; implementing a compact BPE tokenizer for structural-engineering terms; evaluation and failure modes; and a 20-question final examination.

Study package: 8 protected sections · approximately 10 hours of guided study and lab work · tokenizer implementation challenge · mastery checkpoints · final exam.

Modern subword tokenizers usually avoid a literal unknown-word token by splitting unfamiliar terms into smaller pieces; this course therefore treats technical vocabulary primarily as a segmentation and efficiency problem rather than an absolute out-of-vocabulary failure.