Prompt、RAG、继续预训练和微调：什么时候应该改变模型参数

def choose_method(case: dict) -> dict:
    if case["external_tool_wrong"]:
        return {"method": "fix_application", "reason": "错误来自工具或业务链路"}
    if case["facts_change_often"] or case["needs_citation"]:
        return {"method": "rag", "reason": "知识需要更新或引用"}
    if case["few_examples_work"]:
        return {"method": "prompt", "reason": "上下文示例已经能约束行为"}
    if case["domain_corpus_size"] >= 100_000 and case["unknown_terms"]:
        return {"method": "continued_pretraining", "reason": "需要吸收大量领域语言"}
    if case["labeled_examples"] >= 500 and case["stable_task"]:
        return {"method": "sft", "reason": "存在稳定任务和可评估示范"}
    return {"method": "collect_evidence", "reason": "数据和问题证据不足"}


case = {
    "external_tool_wrong": False,
    "facts_change_often": True,
    "needs_citation": True,
    "few_examples_work": False,
    "domain_corpus_size": 0,
    "unknown_terms": False,
    "labeled_examples": 2000,
    "stable_task": True,
}
print(choose_method(case))