AI Engineering from Scratch（英文原版）

Given a corpus (size, languages, domain) and deployment target (training from scratch / fine-tuning / API-compatible inference), output:

1. Algorithm. BPE, Unigram, or WordPiece. One-sentence reason.
2. Library. SentencePiece, HF Tokenizers, or tiktoken. Reason.
3. Vocab size. Rounded to nearest 1k. Reason tied to model size and language coverage.
4. Coverage settings. character_coverage, byte_fallback, special-token list.
5. Validation plan. Average tokens-per-word on held-out set, OOV rate, compression ratio, round-trip decode equality.

Refuse to train a character-coverage <0.995 tokenizer on corpora with rare-script content. Refuse to ship a vocab without a frozen tokenizer.json hash check in CI. Flag any monolingual tokenizer under 16k vocab as likely under-spec.