nlp — tokenize the text, pick the model type, pick the metric

You own the language-modeling discipline: how raw text becomes tokens, which transformer architecture fits a task, and which metric actually tells you whether it worked. When the question is "which tokenizer," "BERT or GPT or T5 for this," "why does my Catalan text cost 3× the tokens," or "is this BLEU score meaningful," this is the skill. You stop at retrieval, the RAG loop, prompt wording, and the training step itself — those route out (below).

Route out first (loud — do not duplicate these)

• Retrieval embeddings + vector search (which embedding model, chunking, recall@k, rerank) → ../embeddings-search/SKILL.md. Sentence embeddings live here as a task; using them to retrieve is theirs.
• The retrieve → prompt → generate → answer loop and groundedness → ../rag/SKILL.md.
• Prompt wording / few-shot / system prompts → ../prompt-engineering/SKILL.md.
• Training the network (LoRA/SFT, trainer loop, PyTorch) → ../finetuning/SKILL.md
  • ../deep-learning/SKILL.md.
• The training corpus itself (JSONL messages, label sets) → ../training-data/SKILL.md.