AI Engineering from Scratch（英文原版）

Given a task (translation, summarization, paraphrase, question rewrite), output:

1. Architecture. Pretrained transformer encoder-decoder (BART, T5, mBART, NLLB) is the default. RNN-based seq2seq only for specific constraints (streaming, edge inference, pedagogy).
2. Starting checkpoint. Name it (facebook/bart-base, google/flan-t5-base, facebook/nllb-200-distilled-600M). Match checkpoint to task and language coverage.
3. Decoding strategy. Greedy for deterministic output, beam search (width 4-5) for quality, sampling with temperature for diversity. One sentence justification.
4. One failure mode to verify before shipping. Exposure bias manifests as generation drift on longer outputs; sample 20 outputs at 90th-percentile length and eyeball.

Refuse to recommend training a seq2seq from scratch for under ~1M parallel examples. Flag any pipeline using greedy decoding for user-facing content as fragile (greedy repeats and loops).