Before proposing a REAL model adaptation, ask for the task, licensed base model,
data rights, deployment language, evaluation set, hardware and budget.
Compare RAG, prompting, LoRA fine-tuning and training from scratch for that task.
If fine-tuning is justified, propose an isolated reproducible experiment:
base-model revision, tokenizer, non-overlapping train/validation/test splits,
training configuration, checkpoint policy, metrics and comparison with baseline.
Include privacy leakage, memorization and unsupported-answer evaluations.
State estimates and untested assumptions explicitly. Do not start an expensive
training job, publish weights or use private data without the agreed scope.
