Models don't read words — they read tokens. A Chinese character is typically 1-2 tokens; an English word is 1-3. The context window is the token limit per request. Inference is using a model; training is teaching one. What you do every day is inference.
You don't need to become a researcher to understand AI models systematically. This series starts from what you can see (tokens, context windows) and works up to self-hosting open-source models — 18 articles covering everything you need to choose models, read benchmarks, and estimate costs.
Models charge by tokens, not characters. The BPE algorithm starts from individual bytes and repeatedly merges the most frequent adjacent pair to build a vocabulary. English 'understanding' might be 1-2 tokens, but Chinese '理解' could take 2-3 — same meaning, higher cost.
Week 2 builds three layers: a token vocabulary with BPE, sequence comparison with dynamic-programming edit distance, and probability approximation with n-grams; PA1 turns regex and BPE into executable work.
Lecture 7 decomposes pretraining into scalable data, subword tokenization, three model objectives, and in-context learning. A general self-supervised objective yields reusable representations; downstream signals specify their use.
Lecture 14 moves from word, character/byte, and subword segmentation to BPE failures and cross-lingual fairness. A tokenizer determines sequence length, compute cost, and the units a model sees; it is not neutral preprocessing.
CS336's first lecture does not treat building a language model from scratch as reenacting every old technique. It separates mechanics, mindset, and intuitions, then uses BPE to show how raw bytes become trainable tokens.