\u4e00\u3001Prompt Engineering\u56db\u5c42\u4f53\u7cfb<\/p>\n
\u7b2c\u4e00\u5c42：\u660e\u786e\u6307\u4ee4\u2014\u2014\u628a\u8bdd\u8bf4\u6e05\u695a<\/p>\n
\u7b2c\u4e8c\u5c42：\u63d0\u4f9b\u4e0a\u4e0b\u6587\u2014\u2014\u7ed9AI\u5b89\u88c5"\u8bb0\u5fc6\u82af\u7247"<\/p>\n
\u7b2c\u4e09\u5c42：\u601d\u7ef4\u94fe(CoT)\u2014\u2014\u6559AI\u4e00\u6b65\u4e00\u6b65\u60f3<\/p>\n
\u7b2c\u56db\u5c42：\u81ea\u6211\u53cd\u601d+\u8f93\u51fa\u6821\u9a8c\u2014\u2014\u8ba9AI\u81ea\u5df1\u627e\u9519\u8bef<\/p>\n
\u4e8c\u3001Prompt\u6a21\u677f\u4f53\u7cfb\u8bbe\u8ba1<\/p>\n
\u4e09\u3001\u7ed3\u6784\u5316\u8f93\u51fa\u63a7\u5236<\/p>\n
\u56db\u3001Few-shot\u793a\u4f8b\u9009\u62e9\u7b56\u7565<\/p>\n
\u4e94\u3001Prompt\u7248\u672c\u7ba1\u7406<\/p>\n
\u516d\u3001A\/B\u6d4b\u8bd5：Prompt\u7684"\u7070\u5ea6\u53d1\u5e03"<\/p>\n
\u4e03\u3001\u6210\u672c\u4e0e\u8d28\u91cf\u7684\u6743\u8861<\/p>\n
\u540c\u6837\u63a5GPT-4\u7684API，\u540c\u4e8b\u7684\u8f93\u51fa\u50cf\u838e\u58eb\u6bd4\u4e9a，\u4f60\u7684\u8f93\u51fa\u50cf\u521a\u5b66\u4f1a\u6253\u5b57\u7684\u5c0f\u5b66\u751f\u3002 \u95ee\u9898\u4e0d（\u5168）\u5728\u6a21\u578b，\u800c\u5728\u4f60\u600e\u4e48\u8ddf\u5b83\u8bf4\u8bdd\u3002Prompt Engineering\u4e0d\u662f\u7384\u5b66，\u662f\u4e00\u95e8\u6b63\u7ecf\u7684\u7cfb\u7edf\u5de5\u7a0b\u3002<\/p>\n
\u4e00\u4e2aPrompt\u8c03\u4e00\u5929，\u4ea7\u51fa\u591a\u51fa\u4e00\u4e2a\u6708\u5de5\u8d44\u2014\u2014\u8fd9\u4e8b\u513f\u6211\u5e72\u8fc7，\u4f60\u4e5f\u80fd\u5e72\u3002<\/p>\n
\u4eca\u5929\u8fd9\u7bc7\u6587\u7ae0，\u6211\u4ece"\u628a\u8bdd\u8bf4\u6e05\u695a"\u5230"\u8ba9AI\u81ea\u5df1\u53cd\u601d\u81ea\u5df1"，\u7528\u4e00\u5957\u56db\u5c42\u65b9\u6cd5\u8bba + \u53ef\u843d\u5730\u7684\u4ee3\u7801，\u5e26\u4f60\u4ecePrompt\u5c0f\u767d\u5230\u80fd\u9762\u8bd5\u522b\u4ebaPrompt\u7684\u6c34\u5e73\u3002<\/p>\n
\u522b\u6025\u7740\u770b\u6a21\u677f，\u5148\u641e\u61c2\u8fd9\u5f20\u56fe\u3002Prompt\u80fd\u529b\u7684\u63d0\u5347\u4e0d\u662f"\u591a\u52a0\u70b9\u63d0\u793a\u8bcd"，\u800c\u662f\u4e00\u5c42\u4e00\u5c42\u5f80\u4e0a\u8d70：<\/p>\n
graph TD
\n L1["🟢 \u7b2c\u4e00\u5c42：\u660e\u786e\u6307\u4ee4<br\/>Tell it WHAT to do"]
\n L2["🟡 \u7b2c\u4e8c\u5c42：\u63d0\u4f9b\u4e0a\u4e0b\u6587<br\/>Tell it WHO it is & WHY"]
\n L3["🟠 \u7b2c\u4e09\u5c42：\u601d\u7ef4\u94fe(CoT)<br\/>Tell it HOW to think"]
\n L4["🔴 \u7b2c\u56db\u5c42：\u81ea\u6211\u53cd\u601d+\u6821\u9a8c<br\/>Let it check ITSELF"]<\/p>\n
L1 –> L2 –> L3 –> L4<\/p>\n
L1 -.- E1["\u6548\u679c：\u52c9\u5f3a\u80fd\u7528，\u7ecf\u5e38\u8dd1\u504f"]
\n L2 -.- E2["\u6548\u679c：\u98ce\u683c\u7edf\u4e00，\u51cf\u5c11\u5e7b\u89c9"]
\n L3 -.- E3["\u6548\u679c：\u63a8\u7406\u51c6\u786e\u7387\u663e\u8457\u63d0\u5347"]
\n L4 -.- E4["\u6548\u679c：\u751f\u4ea7\u7ea7\u53ef\u9760，\u81ea\u7ea0\u9519"]<\/p>\n
style L1 fill:#90EE90,stroke:#333
\n style L2 fill:#FFD700,stroke:#333
\n style L3 fill:#FFA500,stroke:#333
\n style L4 fill:#FF6347,stroke:#333<\/p>\n
\u5212\u91cd\u70b9： \u5927\u90e8\u5206\u4eba\u7684Prompt\u5361\u5728L1\u548cL2\u4e4b\u95f4\u3002\u82b1\u4e00\u5929\u5b66\u4e86"\u4f60\u662fxx\u4e13\u5bb6"\u5c31\u4ee5\u4e3a\u81ea\u5df1Pro\u4e86，\u5176\u5b9e\u521a\u5165\u95e8\u3002<\/p>\n
\u8fd9\u4e00\u5c42\u6700\u7b80\u5355\u4e5f\u6700\u5bb9\u6613\u72af\u9519\u3002\u4e0d\u4fe1？\u6765\u770b\u4e09\u7ec4\u5bf9\u6bd4：<\/p>\n
# \u274c \u70c2Prompt：\u6a21\u7cca\u5f97\u50cf\u4f60\u5988\u8ba9\u4f60"\u53bb\u4e70\u70b9\u4e1c\u897f"
\nbad_prompt = "\u5199\u4e00\u4e2aPython\u51fd\u6570\u5904\u7406\u6570\u636e"<\/p>\n
# \u2705 \u597dPrompt：\u7cbe\u786e\u5f97\u50cf\u5916\u5356\u5907\u6ce8"\u4e0d\u52a0\u9999\u83dc\u4e0d\u653e\u8471\u5c11\u8fa3"
\ngood_prompt = """
\n\u7f16\u5199\u4e00\u4e2aPython\u51fd\u6570 `def clean_data(df: pd.DataFrame) -> pd.DataFrame`：
\n– \u5220\u9664\u6240\u6709\u5305\u542bNaN\u7684\u884c
\n– \u5c06\u65e5\u671f\u5217"date"\u8f6c\u4e3adatetime\u683c\u5f0f
\n– \u5bf9\u6570\u503c\u5217\u505aZ-score\u6807\u51c6\u5316（mean=0, std=1）
\n– \u8fd4\u56de\u5904\u7406\u540e\u7684DataFrame
\n– \u6dfb\u52a0docstring\u548c\u7c7b\u578b\u6ce8\u89e3
\n"""<\/p>\n
\u4f60\u770b，\u597d\u7684Prompt\u5c31\u662f\u5728\u5199"\u6280\u672f\u9700\u6c42\u6587\u6863"\u3002\u51e0\u4e2a\u5173\u952e\u8981\u7d20：<\/p>\n
\u26a0\ufe0f \u907f\u57511：\u7528"\u4e0d\u8981"\u4e0d\u5982\u7528"\u8981" \u4eba\u7c7b\u5bf9\u5426\u5b9a\u53e5\u654f\u611f（\u4e0d\u8981\u78b0\u706b🔥），LLM\u5bf9\u5426\u5b9a\u6307\u4ee4\u7684\u7406\u89e3\u5dee\u5f88\u591a\u3002\u5199"\u8f93\u51faJSON\u683c\u5f0f"\u6bd4"\u4e0d\u8981\u8f93\u51famarkdown"\u6548\u679c\u597d10\u500d\u3002\u539f\u56e0？\u6ce8\u610f\u529b\u673a\u5236\u91cc\u7684\u8d1f\u5411token\u6743\u91cd\u8fdc\u4f4e\u4e8e\u6b63\u5411\u2014\u2014\u8bf4\u4eba\u8bdd\u5c31\u662f：AI\u662f"\u6309\u4f60\u8bf4\u7684\u505a"\u800c\u4e0d\u662f"\u6309\u4f60\u4e0d\u8bf4\u7684\u505a"\u3002<\/p>\n
# \u274c \u5426\u5b9a\u5f0f\u6307\u4ee4\u2014\u2014AI\u53ef\u80fd\u5ffd\u7565"\u4e0d\u8981"\u4e8c\u5b57
\nbad = "\u4e0d\u8981\u4f7f\u7528markdown\u683c\u5f0f，\u4e0d\u8981\u6dfb\u52a0\u591a\u4f59\u89e3\u91ca"<\/p>\n
# \u2705 \u80af\u5b9a\u5f0f\u6307\u4ee4\u2014\u2014\u544a\u8bc9\u5b83\u8981\u4ec0\u4e48
\ngood = "\u4ec5\u8f93\u51fa\u7eafJSON\u5b57\u7b26\u4e32，\u952e\u540d\u4f7f\u7528snake_case\u3002\u4e0d\u8981\u9644\u5e26\u4efb\u4f55\u89e3\u91ca\u6587\u672c\u3002"<\/p>\n
\u8fd9\u4e00\u5c42\u662f\u8d28\u7684\u98de\u8dc3\u3002\u4e0a\u4e0b\u6587 = \u89d2\u8272 + \u80cc\u666f\u4fe1\u606f + \u8f93\u51fa\u53d7\u4f17\u3002<\/p>\n
# L2\u7ea7\u522bPrompt\u6a21\u677f
\nprompt_with_context = """
\n\u3010\u89d2\u8272\u3011\u4f60\u662f\u4e00\u4f4d\u62e5\u670910\u5e74\u7ecf\u9a8c\u7684Python\u540e\u7aef\u67b6\u6784\u5e08，\u64c5\u957f\u5fae\u670d\u52a1\u8bbe\u8ba1\u548c\u9ad8\u5e76\u53d1\u4f18\u5316\u3002<\/p>\n
\u3010\u80cc\u666f\u3011\u6211\u4eec\u6709\u4e00\u4e2a\u7535\u5546\u8ba2\u5355\u7cfb\u7edf，\u65e5\u5747\u8ba2\u5355\u91cf50\u4e07\u3002\u76ee\u524d\u8ba2\u5355\u67e5\u8be2\u63a5\u53e3P99\u5ef6\u8fdf
\n\u8fbe\u5230\u4e862.3\u79d2，\u9700\u8981\u4f18\u5316\u5230200ms\u4ee5\u5185\u3002<\/p>\n
\u3010\u4efb\u52a1\u3011\u5206\u6790\u53ef\u80fd\u7684\u6027\u80fd\u74f6\u9888，\u7ed9\u51fa3\u4e2a\u4f18\u5316\u65b9\u6848，\u6bcf\u4e2a\u65b9\u6848\u5305\u542b：
\n– \u65b9\u6848\u63cf\u8ff0（50\u5b57\u4ee5\u5185）
\n– \u9884\u8ba1\u5ef6\u8fdf\u964d\u4f4e\u5e45\u5ea6
\n– \u5b9e\u73b0\u590d\u6742\u5ea6（\u9ad8\/\u4e2d\/\u4f4e）
\n– \u6f5c\u5728\u98ce\u9669<\/p>\n
\u3010\u53d7\u4f17\u3011\u8f93\u51fa\u7ed9\u6280\u672f\u7ecf\u7406\u9605\u8bfb，\u8981\u6c42\u6280\u672f\u51c6\u786e\u4f46\u4e0d\u5931\u53ef\u8bfb\u6027\u3002
\n"""<\/p>\n
💡 \u6548\u7387\u6280\u5de71：\u89d2\u8272\u8bbe\u5b9a\u7701\u638980%\u7684\u683c\u5f0f\u8c03\u6574 \u4e0e\u5176\u519910\u884c"\u4f60\u7684\u8f93\u51fa\u5e94\u8be5\u7528\u4ec0\u4e48\u683c\u5f0f"，\u4e0d\u5982\u4e00\u53e5"\u4f60\u662f\u4e00\u4f4d\u8d44\u6df1\u6280\u672f\u535a\u5ba2\u4f5c\u8005"\u2014\u2014\u6a21\u578b\u81ea\u5df1\u77e5\u9053\u535a\u5ba2\u8be5\u957f\u4ec0\u4e48\u6837\u3002\u8fd9\u53eb\u5185\u5316\u77e5\u8bc6\u89e6\u53d1（Internalized Knowledge Trigger），GPT\u7cfb\u5217\u5728\u89d2\u8272\u8bbe\u5b9a\u540e\u4f1a\u81ea\u52a8\u6fc0\u6d3b\u5bf9\u5e94\u7684\u5199\u4f5c\u98ce\u683c\u795e\u7ecf\u5143\u3002<\/p>\n
\u5f88\u591a\u4eba\u95ee：\u201c\u4e3a\u4ec0\u4e48\u52a0\u4e86\u2019Let\u2019s think step by step\u2019\u6548\u679c\u5c31\u53d8\u597d？\u201d<\/p>\n
\u56e0\u4e3aLLM\u672c\u8d28\u4e0a\u662f\u6982\u7387\u9884\u6d4b\u4e0b\u4e00\u4e2atoken\u7684\u673a\u5668\u3002\u4e0d\u7ed9\u63a8\u7406\u8def\u5f84，\u5b83\u5c31"\u731c"\u7b54\u6848；\u7ed9\u4e86\u8def\u5f84，\u5b83\u5c31"\u7b97"\u7b54\u6848\u3002<\/p>\n
graph LR
\n A["\u95ee\u9898\u8f93\u5165"] –> B["\u274c \u65e0CoT<br\/>\u76f4\u63a5\u8e66\u7b54\u6848"]
\n A –> C["\u2705 \u6709CoT<br\/>Step 1: \u7406\u89e3\u95ee\u9898"]
\n C –> D["Step 2: \u62c6\u89e3\u5b50\u95ee\u9898"]
\n D –> E["Step 3: \u9010\u6b65\u63a8\u7406"]
\n E –> F["Step 4: \u6574\u5408\u7b54\u6848"]
\n F –> G["\u8f93\u51fa\u6700\u7ec8\u7ed3\u679c"]<\/p>\n
B -.- B1["\u6b63\u786e\u7b54\u6848\u6982\u7387: 60%"]
\n G -.- G1["\u6b63\u786e\u7b54\u6848\u6982\u7387: 90%+"]<\/p>\n
style B fill:#FFB6C1
\n style G fill:#90EE90<\/p>\n
\u6765\u770b\u4ee3\u7801\u5b9e\u73b0\u4e00\u4e2a\u5b8c\u6574\u7684CoT Prompt\u6a21\u677f：<\/p>\n
def build_cot_prompt(question: str, domain: str = "general") -> str:
\n """
\n \u6784\u5efa\u601d\u7ef4\u94fePrompt\u6a21\u677f
\n """
\n cot_templates = {
\n "code": """
\n\u3010\u4efb\u52a1\u3011{question}<\/p>\n
\u8bf7\u6309\u4ee5\u4e0b\u6b65\u9aa4\u9010\u6b65\u63a8\u7406：<\/p>\n
\u7b2c1\u6b65 – \u9700\u6c42\u5206\u6790：\u660e\u786e\u8f93\u5165\u8f93\u51fa\u662f\u4ec0\u4e48？\u8fb9\u754c\u6761\u4ef6\u6709\u54ea\u4e9b？
\n\u7b2c2\u6b65 – \u7b97\u6cd5\u9009\u62e9：\u54ea\u79cd\u6570\u636e\u7ed3\u6784\/\u7b97\u6cd5\u6700\u4f18？\u65f6\u95f4\u590d\u6742\u5ea6\u662f\u591a\u5c11？
\n\u7b2c3\u6b65 – \u4f2a\u4ee3\u7801：\u5199\u51fa\u6838\u5fc3\u903b\u8f91\u7684\u4f2a\u4ee3\u7801
\n\u7b2c4\u6b65 – \u7f16\u7801\u5b9e\u73b0：\u7ed9\u51fa\u53ef\u8fd0\u884c\u7684Python\u4ee3\u7801
\n\u7b2c5\u6b65 – \u6d4b\u8bd5\u7528\u4f8b：\u63d0\u4f9b3\u4e2a\u6d4b\u8bd5\u7528\u4f8b\u53ca\u5176\u671f\u671b\u8f93\u51fa
\n\u7b2c6\u6b65 – \u590d\u6742\u5ea6\u5206\u6790：\u65f6\u95f4\u548c\u7a7a\u95f4\u590d\u6742\u5ea6<\/p>\n
\u26a0\ufe0f \u6bcf\u4e00\u6b65\u90fd\u5fc5\u987b\u8f93\u51fa\u663e\u5f0f\u6807\u8bb0，\u683c\u5f0f\u4e3a\u300c\u7b2cX\u6b65：\u300d，\u4e0d\u53ef\u8df3\u8fc7\u4efb\u4f55\u4e00\u6b65\u3002
\n""",
\n "analysis": """
\n\u3010\u4efb\u52a1\u3011{question}<\/p>\n
\u8bf7\u6309\u4ee5\u4e0b\u6846\u67b6\u5206\u6790：<\/p>\n
1. \u80cc\u666f\u68b3\u7406：\u8fd9\u4ef6\u4e8b\u7684\u80cc\u666f\u548c\u4e0a\u4e0b\u6587\u662f\u4ec0\u4e48？
\n2. \u5173\u952e\u56e0\u7d20：\u5f71\u54cd\u7ed3\u679c\u7684\u6838\u5fc3\u53d8\u91cf\u6709\u54ea\u4e9b？
\n3. \u591a\u89d2\u5ea6\u5206\u6790：\u81f3\u5c11\u4ece3\u4e2a\u4e0d\u540c\u89d2\u5ea6\u5206\u6790
\n4. \u5229\u5f0a\u6743\u8861：\u5217\u51fa\u6bcf\u4e2a\u65b9\u6848\u7684\u4f18\u7f3a\u70b9
\n5. \u7ed3\u8bba\u5efa\u8bae：\u7ed9\u51fa\u6700\u7ec8\u5efa\u8bae\u5e76\u89e3\u91ca\u7406\u7531
\n"""
\n }
\n template = cot_templates.get(domain, cot_templates["analysis"])
\n return template.format(question=question)<\/p>\n
# \u4f7f\u7528\u793a\u4f8b
\nquestion = "\u8bbe\u8ba1\u4e00\u4e2a\u5206\u5e03\u5f0f\u9650\u6d41\u5668，\u8981\u6c42\u652f\u6301\u6ed1\u52a8\u7a97\u53e3\u7b97\u6cd5"
\ncot_prompt = build_cot_prompt(question, domain="code")
\nprint(cot_prompt)<\/p>\n
\u26a0\ufe0f \u907f\u57512：CoT\u4e0d\u662f\u8d8a\u957f\u8d8a\u597d\u2014\u2014"\u8fc7\u5ea6\u63a8\u7406"\u9677\u9631 \u6211\u89c1\u8fc7\u6709\u4eba\u7ed9\u7b80\u5355\u4efb\u52a1（\u5982"\u7ffb\u8bd1\u4e00\u53e5\u8bdd"）\u51998\u6b65CoT\u3002\u7ed3\u679c\u5462？\u6a21\u578b\u5f00\u59cb\u8111\u8865\u4e0d\u5b58\u5728\u7684\u95ee\u9898，\u7b54\u6848\u53cd\u800c\u53d8\u5dee\u4e86\u3002CoT\u7684\u6536\u76ca\u66f2\u7ebf\u662f\u5012U\u578b\u7684\u2014\u2014\u7b80\u5355\u4efb\u52a1\u7528L1\/L2\u5c31\u591f\u4e86，\u590d\u6742\u63a8\u7406（\u6570\u5b66\u3001\u4ee3\u7801\u3001\u903b\u8f91\u5206\u6790）\u624d\u4e0aCoT\u3002<\/p>\n
💡 \u6548\u7387\u6280\u5de72：\u52a8\u6001\u9009\u62e9\u63a8\u7406\u6df1\u5ea6 \u7528\u4e00\u4e2a\u5206\u7c7b\u5668\u5224\u65ad\u4efb\u52a1\u590d\u6742\u5ea6，\u518d\u51b3\u5b9a\u7528\u51e0\u5c42Prompt：<\/p>\n
def select_prompt_level(task: str) -> int:
\n """\u6839\u636e\u4efb\u52a1\u590d\u6742\u5ea6\u81ea\u52a8\u9009\u62e9Prompt\u5c42\u7ea7"""
\n complex_keywords = [
\n "\u8bbe\u8ba1", "\u4f18\u5316", "\u5bf9\u6bd4\u5206\u6790", "debug", "\u67b6\u6784",
\n "\u7b97\u6cd5", "\u63a8\u7406", "\u591a\u6b65\u9aa4", "review", "\u6743\u8861"
\n ]
\n simple_keywords = [
\n "\u7ffb\u8bd1", "\u603b\u7ed3", "\u63d0\u53d6", "\u683c\u5f0f\u5316", "\u91cd\u5199",
\n "\u7f29\u5199", "\u6807\u7b7e", "\u5206\u7c7b"
\n ]<\/p>\n
task_lower = task.lower()<\/p>\n
if any(kw in task_lower for kw in complex_keywords):
\n return 4 # L4：\u5b8c\u6574CoT+\u81ea\u6211\u53cd\u601d
\n elif any(kw in task_lower for kw in simple_keywords):
\n return 2 # L2：\u4e0a\u4e0b\u6587\u5c31\u591f\u4e86
\n else:
\n return 3 # L3：\u6807\u51c6CoT<\/p>\n
L4\u662f\u751f\u4ea7\u73af\u5883\u7684\u5206\u6c34\u5cad\u3002\u6838\u5fc3\u903b\u8f91：\u8ba9\u6a21\u578b\u751f\u6210 \u2192 \u8ba9\u6a21\u578b\u81ea\u5df1\u8bc4\u5ba1 \u2192 \u8ba9\u6a21\u578b\u4fee\u6b63\u3002<\/p>\n
class SelfReflectivePrompt:
\n """L4\u81ea\u53cd\u601dPrompt\u6846\u67b6"""<\/p>\n
def __init__(self, model_func):
\n self.model_func = model_func # \u4f60\u7684LLM\u8c03\u7528\u51fd\u6570<\/p>\n
def generate(self, task: str) -> dict:
\n # Phase 1: \u521d\u59cb\u751f\u6210
\n gen_prompt = f"""
\n{task}<\/p>\n
\u8981\u6c42：
\n– \u8f93\u51fa\u5b8c\u6574\u7b54\u6848
\n– \u5728\u6bcf\u4e2a\u5173\u952e\u5224\u65ad\u5904\u7528\u300c\u3010\u5173\u952e\u5224\u65ad\u3011\u300d\u6807\u8bb0
\n– \u5728\u6bcf\u4e2a\u5047\u8bbe\u5904\u7528\u300c\u3010\u5047\u8bbe\u3011\u300d\u6807\u8bb0\u4f60\u7684\u524d\u63d0
\n"""
\n initial_output = self.model_func(gen_prompt)<\/p>\n
# Phase 2: \u81ea\u6211\u8bc4\u5ba1
\n review_prompt = f"""
\n\u8bf7\u4e25\u683c\u5ba1\u67e5\u4ee5\u4e0b\u8f93\u51fa，\u627e\u51fa\u6240\u6709\u95ee\u9898：<\/p>\n
\u3010\u539f\u59cb\u8f93\u51fa\u3011
\n{initial_output}<\/p>\n
\u3010\u5ba1\u67e5\u6807\u51c6\u3011
\n1. \u4e8b\u5b9e\u6027\u9519\u8bef：\u6709\u6ca1\u6709\u4e0e\u516c\u8ba4\u77e5\u8bc6\u77db\u76fe\u7684\u5730\u65b9？
\n2. \u903b\u8f91\u6f0f\u6d1e：\u63a8\u7406\u94fe\u6761\u6709\u6ca1\u6709\u8df3\u8dc3\u6216\u77db\u76fe？
\n3. \u5b8c\u6574\u6027：\u6709\u6ca1\u6709\u9057\u6f0f\u5173\u952e\u4fe1\u606f？
\n4. \u8fb9\u754c\u6761\u4ef6：\u6781\u7aef\u60c5\u51b5\u662f\u5426\u5904\u7406？<\/p>\n
\u8bf7\u5217\u51fa\u6240\u6709\u300c\u53d1\u73b0\u7684\u95ee\u9898\u300d，\u6bcf\u6761\u683c\u5f0f：
\n– [\u4e25\u91cd\u7a0b\u5ea6: \u9ad8\/\u4e2d\/\u4f4e] \u95ee\u9898\u63cf\u8ff0 \u2192 \u5efa\u8bae\u4fee\u6b63
\n"""
\n review = self.model_func(review_prompt)<\/p>\n
# Phase 3: \u57fa\u4e8e\u8bc4\u5ba1\u4fee\u6b63
\n fix_prompt = f"""
\n\u6839\u636e\u4ee5\u4e0b\u8bc4\u5ba1\u610f\u89c1\u4fee\u6b63\u4f60\u7684\u539f\u59cb\u8f93\u51fa：<\/p>\n
\u3010\u539f\u59cb\u8f93\u51fa\u3011
\n{initial_output}<\/p>\n
\u3010\u8bc4\u5ba1\u610f\u89c1\u3011
\n{review}<\/p>\n
\u3010\u4fee\u6b63\u8981\u6c42\u3011
\n– \u9010\u6761\u5904\u7406\u6240\u6709\u8bc4\u5ba1\u610f\u89c1
\n– \u8f93\u51fa\u5b8c\u6574\u7684\u4fee\u6b63\u540e\u7248\u672c
\n– \u5728\u6587\u672b\u5217\u51fa\u300c\u4fee\u6b63\u6e05\u5355\u300d
\n"""
\n final_output = self.model_func(fix_prompt)<\/p>\n
return {
\n "initial": initial_output,
\n "review": review,
\n "final": final_output
\n }<\/p>\n
L4\u7684\u4ee3\u4ef7\u662f\u663e\u800c\u6613\u89c1\u7684\u2014\u2014\u4e00\u6b21\u751f\u6210\u53d8\u6210\u4e09\u6b21\u8c03\u7528，token\u6d88\u8017\u7ffb3\u500d\u3002\u4f46\u5982\u679c\u4f60\u5728\u505a\u4e00\u4e2a\u6bcf\u5929\u8dd110\u4e07\u6b21\u7684\u6838\u5fc3\u4e1a\u52a1\u903b\u8f91，\u8fd93\u500d\u6210\u672c\u6362\u6765\u7684\u662f\u4ece"90%\u51c6\u786e\u7387"\u5230"99.5%\u51c6\u786e\u7387"\u7684\u63d0\u5347\u2014\u2014\u503c\u4e0d\u503c，\u4f60\u7b97\u4e00\u4e0b\u51fa\u9519\u7684\u5ba2\u8bc9\u6210\u672c\u5c31\u77e5\u9053\u4e86\u3002<\/p>\n
\u26a0\ufe0f \u907f\u57513：\u81ea\u8bc4\u5ba1\u4e0d\u7b49\u4e8e"\u81ea\u55e8"\u2014\u2014\u6a21\u578b\u5f88\u96be\u53d1\u73b0\u81ea\u5df1\u7684\u7cfb\u7edf\u6027\u504f\u89c1 \u6362\u4e2a\u6bd4\u55bb\u4f60\u5c31\u61c2\u4e86：\u8ba9\u4e00\u4e2a\u5e7f\u4e1c\u4eba\u8bc4\u5ba1\u81ea\u5df1\u5199\u7684"\u6b63\u5b97\u5ddd\u83dc\u83dc\u8c31"，\u9760\u8c31\u5417？\u6240\u4ee5L4\u7684\u6b63\u786e\u6253\u5f00\u65b9\u5f0f\u662f：\u7528\u4e0d\u540c\u6a21\u578b\u505a\u8bc4\u5ba1（\u5982\u7528Claude\u8bc4\u5ba1GPT\u7684\u8f93\u51fa，\u6216\u7528GPT-4\u8bc4\u5ba1GPT-3.5\u7684\u8f93\u51fa），\u8fd9\u53eb"\u4ea4\u53c9\u9a8c\u8bc1"\u3002<\/p>\n
def cross_model_review(generate_model, review_model, task: str) -> dict:
\n """\u7528\u4e0d\u540c\u6a21\u578b\u505a\u4ea4\u53c9\u8bc4\u5ba1"""
\n output = generate_model(task)
\n review = review_model(f"""
\n\u8bf7\u4f5c\u4e3a\u72ec\u7acb\u8bc4\u5ba1\u5458，\u5ba1\u67e5\u4ee5\u4e0bAI\u8f93\u51fa：
\n\u3010\u8f93\u51fa\u3011
\n{output}
\n\u3010\u8bc4\u5ba1\u8981\u70b9\u3011
\n\u9010\u6761\u6307\u51fa\u6240\u6709\u95ee\u9898，\u4e0d\u8981\u987e\u53ca"\u540c\u884c\u9762\u5b50"\u3002
\n """)
\n return {"output": output, "review": review}<\/p>\n
\u641e\u61c2\u56db\u5c42\u4e4b\u540e，\u6765\u770b\u600e\u4e48\u628a\u8fd9\u4e9b\u4e1c\u897f\u5de5\u7a0b\u5316\u3002\u6211\u8e29\u4e86\u534a\u5e74\u5751\u603b\u7ed3\u51fa\u7684Prompt\u6a21\u677f\u4e94\u8981\u7d20：<\/p>\n
graph TD
\n T["📋 Prompt\u6a21\u677f\u4e94\u8981\u7d20"]
\n T –> R["🎭 \u89d2\u8272(Role)<br\/>\u4f60\u662f\u4ec0\u4e48\u8eab\u4efd？"]
\n T –> TK["📝 \u4efb\u52a1(Task)<br\/>\u5177\u4f53\u8981\u505a\u4ec0\u4e48？"]
\n T –> F["📐 \u683c\u5f0f(Format)<br\/>\u8f93\u51fa\u957f\u4ec0\u4e48\u6837？"]
\n T –> C["🔒 \u7ea6\u675f(Constraints)<br\/>\u7edd\u5bf9\u4e0d\u80fd\u5e72\u4ec0\u4e48？"]
\n T –> E["🎯 \u793a\u4f8b(Examples)<br\/>\u505a\u5f97\u597d\u662f\u4ec0\u4e48\u6837？"]<\/p>\n
R -.- R1["\u6fc0\u6d3b\u9886\u57df\u77e5\u8bc6"]
\n TK -.- TK1["\u754c\u5b9a\u4ea7\u51fa\u8fb9\u754c"]
\n F -.- F1["\u63a7\u5236\u8f93\u51fa\u7ed3\u6784"]
\n C -.- C1["\u9632\u6b62\u8dd1\u504f"]
\n E -.- E1["Few-shot\u5bf9\u9f50"]<\/p>\n
style T fill:#4A90D9,color:#fff<\/p>\n
\u76f4\u63a5\u4e0a\u53ef\u7528\u7684Prompt\u6a21\u677f\u7ba1\u7406\u7c7b：<\/p>\n
from dataclasses import dataclass, field
\nfrom typing import Optional
\nimport json<\/p>\n
@dataclass
\nclass PromptTemplate:
\n """Prompt\u6a21\u677f\u6570\u636e\u7c7b"""
\n name: str
\n role: str
\n task: str
\n format_spec: str
\n constraints: list[str] = field(default_factory=list)
\n examples: list[dict] = field(default_factory=list)
\n version: str = "1.0.0"<\/p>\n
def build(self, **kwargs) -> str:
\n """\u6784\u5efa\u5b8c\u6574Prompt"""
\n parts = []<\/p>\n
if self.role:
\n parts.append(f"\u3010\u89d2\u8272\u3011{self.role}")<\/p>\n
task_text = self.task.format(**kwargs) if kwargs else self.task
\n parts.append(f"\u3010\u4efb\u52a1\u3011{task_text}")<\/p>\n
if self.format_spec:
\n parts.append(f"\u3010\u8f93\u51fa\u683c\u5f0f\u3011{self.format_spec}")<\/p>\n
if self.constraints:
\n constraints_text = "\\\\n".join(f"- {c}" for c in self.constraints)
\n parts.append(f"\u3010\u7ea6\u675f\u6761\u4ef6\u3011\\\\n{constraints_text}")<\/p>\n
if self.examples:
\n examples_text = ""
\n for i, ex in enumerate(self.examples, 1):
\n examples_text += f"\\\\n\u793a\u4f8b{i}：\\\\n\u8f93\u5165：{ex['input']}\\\\n\u8f93\u51fa：{ex['output']}\\\\n"
\n parts.append(f"\u3010\u53c2\u8003\u793a\u4f8b\u3011{examples_text}")<\/p>\n
return "\\\\n\\\\n".join(parts)<\/p>\n
# ===== \u5b9e\u9645\u4f7f\u7528 =====
\ncode_review_template = PromptTemplate(
\n name="\u4ee3\u7801\u5ba1\u67e5\u6a21\u677f",
\n role="\u4f60\u662f\u4e00\u4f4d\u8d44\u6df1Python\u4ee3\u7801\u5ba1\u67e5\u4e13\u5bb6，\u7cbe\u901aPEP 8\u89c4\u8303\u548c\u8bbe\u8ba1\u6a21\u5f0f",
\n task="\u5ba1\u67e5\u4ee5\u4e0bPython\u4ee3\u7801，\u7ed9\u51fa\u8be6\u7ec6\u7684\u6539\u8fdb\u5efa\u8bae：\\\\n```python\\\\n{code}\\\\n```",
\n format_spec="""
\n\u8f93\u51faJSON\u683c\u5f0f：
\n{
\n "overall_score": 0-100,
\n "issues": [
\n {"severity": "\u9ad8\/\u4e2d\/\u4f4e", "line": \u884c\u53f7, "description": "\u95ee\u9898\u63cf\u8ff0",
\n "suggestion": "\u6539\u8fdb\u5efa\u8bae", "rule": "\u8fdd\u53cd\u7684\u89c4\u8303"}
\n ],
\n "strengths": ["\u4f18\u70b91", "\u4f18\u70b92"],
\n "refactored_code": "\u91cd\u6784\u540e\u7684\u4ee3\u7801\u5b57\u7b26\u4e32"
\n}
\n""",
\n constraints=[
\n "\u4e0d\u4fee\u6539\u4ee3\u7801\u7684\u4e1a\u52a1\u903b\u8f91",
\n "\u6bcf\u4e2aissue\u5fc5\u987b\u5f15\u7528\u5177\u4f53\u7684PEP\u6216\u8bbe\u8ba1\u6a21\u5f0f",
\n "\u91cd\u6784\u4ee3\u7801\u5fc5\u987b\u53ef\u8fd0\u884c",
\n "\u4e0d\u8981\u8f93\u51faJSON\u4e4b\u5916\u7684\u4efb\u4f55\u6587\u672c"
\n ],
\n examples=[{
\n "input": "def f(x): return x*2",
\n "output": '{"overall_score": 45, "issues": […]}'
\n }]
\n)<\/p>\n
# \u6784\u5efaPrompt
\ncode = "def calc(a,b):\\\\n return a+b"
\nfinal_prompt = code_review_template.build(code=code)
\nprint(final_prompt)<\/p>\n
💡 \u6548\u7387\u6280\u5de73：\u6a21\u677f\u7ee7\u627f\u2014\u2014\u5b50\u6a21\u677f\u8986\u76d6\u7236\u6a21\u677f\u7684\u7ea6\u675f \u4f60\u4f1a\u53d1\u73b0\u4e0d\u540c\u9879\u76ee\u7684\u5ba1\u67e5\u6807\u51c6\u4e0d\u4e00\u6837\u3002\u522b\u590d\u5236\u7c98\u8d34，\u7528\u7ee7\u627f：<\/p>\n
class PromptTemplateLibrary:
\n """Prompt\u6a21\u677f\u5e93，\u652f\u6301\u7ee7\u627f\u548c\u7248\u672c\u7ba1\u7406"""<\/p>\n
def __init__(self):
\n self._templates: dict[str, PromptTemplate] = {}<\/p>\n
def register(self, template: PromptTemplate):
\n self._templates[template.name] = template<\/p>\n
def inherit(self, base_name: str, new_name: str,
\n overrides: dict) -> PromptTemplate:
\n """\u57fa\u4e8e\u5df2\u6709\u6a21\u677f\u521b\u5efa\u53d8\u4f53"""
\n base = self._templates[base_name]
\n new_data = {
\n "name": new_name,
\n "role": base.role,
\n "task": base.task,
\n "format_spec": base.format_spec,
\n "constraints": list(base.constraints),
\n "examples": list(base.examples),
\n "version": f"{base.version}+{overrides.get('version_suffix', 'variant')}"
\n }
\n new_data.update({k: v for k, v in overrides.items()
\n if k != 'version_suffix'})
\n new_template = PromptTemplate(**new_data)
\n self.register(new_template)
\n return new_template<\/p>\n
# \u4f7f\u7528\u793a\u4f8b
\nlib = PromptTemplateLibrary()
\nlib.register(code_review_template)<\/p>\n
security_review = lib.inherit(
\n "\u4ee3\u7801\u5ba1\u67e5\u6a21\u677f",
\n "\u5b89\u5168\u5ba1\u67e5\u6a21\u677f",
\n {
\n "role": "\u4f60\u662f\u4e00\u4f4d\u5e94\u7528\u5b89\u5168\u4e13\u5bb6，\u7cbe\u901aOWASP Top 10\u548cCWE",
\n "constraints": [
\n "\u91cd\u70b9\u5173\u6ce8SQL\u6ce8\u5165\u3001XSS\u3001CSRF\u7b49\u5b89\u5168\u6f0f\u6d1e",
\n "\u6bcf\u4e2a\u6f0f\u6d1e\u5fc5\u987b\u7ed9\u51faCWE\u7f16\u53f7",
\n "\u63d0\u4f9b\u5177\u4f53\u4fee\u590d\u4ee3\u7801"
\n ]
\n }
\n)<\/p>\n
\u201c\u7ed9\u6211\u8f93\u51faJSON"\u548c"\u7ed9\u6211\u8f93\u51fa\u7b26\u5408\u8fd9\u4e2aJSON Schema\u7684JSON\u201d\u2014\u2014\u6548\u679c\u5929\u5dee\u5730\u522b\u3002<\/p>\n
import json
\nfrom pydantic import BaseModel, Field
\nfrom typing import Optional<\/p>\n
class ProductReview(BaseModel):
\n """\u7ed3\u6784\u5316\u8f93\u51fa\u7684Pydantic\u6a21\u578b"""
\n product_name: str = Field(description="\u5546\u54c1\u540d\u79f0")
\n sentiment: str = Field(description="\u60c5\u611f\u503e\u5411", pattern="^(\u6b63\u9762|\u8d1f\u9762|\u4e2d\u6027)$")
\n score: float = Field(description="\u8bc4\u5206", ge=0, le=5)
\n key_points: list[str] = Field(description="\u5173\u952e\u8bc4\u4ef7\u8981\u70b9", min_items=1)
\n summary: str = Field(description="\u4e00\u53e5\u8bdd\u603b\u7ed3", max_length=100)
\n category: Optional[str] = Field(None, description="\u5546\u54c1\u5206\u7c7b")<\/p>\n
def build_structured_prompt(task: str, schema_model) -> str:
\n """
\n \u6784\u5efa\u5e26JSON Schema\u7ea6\u675f\u7684Prompt
\n \u6838\u5fc3\u601d\u8def：\u628aPydantic Schema\u8f6c\u6210\u81ea\u7136\u8bed\u8a00\u7ea6\u675f + JSON\u6a21\u677f
\n """
\n schema = schema_model.model_json_schema()
\n properties = schema.get("properties", {})<\/p>\n
# \u751f\u6210\u5b57\u6bb5\u8bf4\u660e
\n field_descriptions = []
\n for field_name, field_info in properties.items():
\n desc = field_info.get("description", "")
\n field_type = field_info.get("type", "string")
\n required = field_name in schema.get("required", [])<\/p>\n
# Pattern\u7ea6\u675f
\n pattern_str = ""
\n if "pattern" in field_info:
\n pattern_str = f" (\u5fc5\u987b\u5339\u914d: {field_info['pattern']})"<\/p>\n
# \u6570\u503c\u7ea6\u675f
\n range_str = ""
\n if "minimum" in field_info:
\n range_str += f" >= {field_info['minimum']}"
\n if "maximum" in field_info:
\n range_str += f" <= {field_info['maximum']}"<\/p>\n
req_mark = "\u3010\u5fc5\u586b\u3011" if required else "\u3010\u53ef\u9009\u3011"
\n field_descriptions.append(
\n f" – {req_mark} {field_name} ({field_type}{range_str}): {desc}{pattern_str}"
\n )<\/p>\n
prompt = f"""
\n{task}<\/p>\n
\u4e25\u683c\u6309\u4ee5\u4e0bJSON Schema\u8f93\u51fa，\u4e0d\u8981\u6dfb\u52a0\u4efb\u4f55\u89e3\u91ca\u6587\u5b57：<\/p>\n
{json.dumps(schema, ensure_ascii=False, indent=2)}<\/p>\n
\u5b57\u6bb5\u7ea6\u675f\u8bf4\u660e：
\n{"".join(field_descriptions)}<\/p>\n
\u26a0\ufe0f \u8f93\u51fa\u5fc5\u987b\u662f\u7eafJSON，\u80fd\u88ab json.loads() \u76f4\u63a5\u89e3\u6790\u3002
\n"""
\n return prompt<\/p>\n
# \u4f7f\u7528
\ntask = "\u5206\u6790\u4ee5\u4e0b\u5546\u54c1\u8bc4\u8bba\u7684\u60c5\u611f：'\u8fd9\u4e2a\u624b\u673a\u62cd\u7167\u771f\u4e0d\u9519，\u4f46\u7535\u6c60\u7eed\u822a\u592a\u5dee\u4e86'"
\nstructured_prompt = build_structured_prompt(task, ProductReview)
\nprint(structured_prompt)<\/p>\n
XML\u6807\u7b7e\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u7ea6\u675f\u5de5\u5177\u2014\u2014LLM\u5bf9XML\u7ed3\u6784\u7684\u7406\u89e3\u51fa\u5947\u5730\u597d：<\/p>\n
def xml_constrained_prompt(text: str) -> str:
\n """\u4f7f\u7528XML\u6807\u7b7e\u5f3a\u5236\u7ea6\u675f\u8f93\u51fa\u683c\u5f0f"""
\n return f"""
\n\u5206\u6790\u4ee5\u4e0b\u6587\u672c\u7684\u60c5\u611f\u3001\u4e3b\u9898\u548c\u6458\u8981\u3002<\/p>\n
<text>
\n{text}
\n<\/text><\/p>\n
\u4e25\u683c\u6309\u4ee5\u4e0bXML\u683c\u5f0f\u8f93\u51fa，\u4e0d\u8981\u6709\u4efb\u4f55\u989d\u5916\u5185\u5bb9：<\/p>\n
<analysis>
\n <sentiment>\u6b63\u9762\/\u8d1f\u9762\/\u4e2d\u6027<\/sentiment>
\n <confidence>0\u52301\u4e4b\u95f4\u7684\u5c0f\u6570<\/confidence>
\n <topics>
\n <topic>\u4e3b\u98981<\/topic>
\n <topic>\u4e3b\u98982<\/topic>
\n <\/topics>
\n <summary>\u4e0d\u8d85\u8fc750\u5b57\u7684\u6458\u8981<\/summary>
\n<\/analysis><\/p>\n
\u6ce8\u610f：XML\u6807\u7b7e\u5fc5\u987b\u5b8c\u6574\u95ed\u5408，\u4e0d\u8981\u9057\u6f0f\u4efb\u4f55\u6807\u7b7e\u3002
\n"""<\/p>\n
Few-shot\u4e0d\u662f\u968f\u4fbf\u6254\u51e0\u4e2a\u4f8b\u5b50\u3002\u4f8b\u5b50\u9009\u9519\u4e86，\u6548\u679c\u8fd8\u4e0d\u5982Zero-shot\u3002<\/p>\n
\u5173\u952e\u95ee\u9898：\u4f60\u624b\u5934100\u4e2a\u793a\u4f8b，\u9009\u54ea3\u4e2a\u653e\u8fdbPrompt？ \u7b54\u6848\u662f：\u9009\u548c\u5f53\u524d\u8f93\u5165\u8bed\u4e49\u6700\u76f8\u4f3c\u7684\u3002<\/p>\n
import numpy as np
\nfrom sklearn.metrics.pairwise import cosine_similarity
\nfrom typing import Optional<\/p>\n
class FewShotSelector:
\n """
\n Few-shot\u793a\u4f8b\u9009\u62e9\u5668
\n \u7b56\u7565：\u57fa\u4e8eembedding\u7684\u8bed\u4e49\u76f8\u4f3c\u5ea6\u81ea\u52a8\u9009\u62e9\u6700\u4f73\u793a\u4f8b
\n """<\/p>\n
def __init__(self, examples: list[dict],
\n embedding_func: Optional[callable] = None):
\n """
\n Args:
\n examples: [{"input": "…", "output": "…"}]
\n embedding_func: \u6587\u672c \u2192 \u5411\u91cf\u7684\u51fd\u6570
\n """
\n self.examples = examples
\n self._embed = embedding_func or self._default_embed
\n self._example_embeddings = None
\n self._precompute()<\/p>\n
def _default_embed(self, text: str) -> np.ndarray:
\n """
\n \u9ed8\u8ba4embedding\u65b9\u6cd5（\u751f\u4ea7\u73af\u5883\u8bf7\u66ff\u6362\u4e3aOpenAI API\u8c03\u7528）
\n \u8fd9\u91cc\u7528\u7b80\u5355\u7684\u5b57\u7b26\u7ea7TF\u4f5c\u4e3a\u6f14\u793a
\n """
\n # \u5b9e\u9645\u4f7f\u7528\u65f6\u66ff\u6362\u4e3a：
\n # response = openai.Embedding.create(
\n # model="text-embedding-3-small", input=text
\n # )
\n # return np.array(response.data[0].embedding)<\/p>\n
# \u6f14\u793a\u7528：\u7b80\u5355\u7684bag-of-chars
\n chars = set('abcdefghijklmnopqrstuvwxyz ')
\n vector = np.zeros(len(chars))
\n text_lower = text.lower()
\n for i, c in enumerate(chars):
\n vector[i] = text_lower.count(c) \/ max(len(text_lower), 1)
\n return vector<\/p>\n
def _precompute(self):
\n """\u9884\u8ba1\u7b97\u6240\u6709\u793a\u4f8b\u7684embedding"""
\n self._example_embeddings = np.array([
\n self._embed(ex["input"]) for ex in self.examples
\n ])<\/p>\n
def select(self, query: str, k: int = 3,
\n diversity_weight: float = 0.2) -> list[dict]:
\n """
\n \u9009\u62e9k\u4e2a\u6700\u4f73\u793a\u4f8b<\/p>\n
Args:
\n query: \u5f53\u524d\u8f93\u5165
\n k: \u9009\u62e9\u6570\u91cf
\n diversity_weight: \u591a\u6837\u6027\u6743\u91cd（0=\u7eaf\u76f8\u4f3c\u5ea6，1=\u7eaf\u591a\u6837\u6027）
\n """
\n query_embedding = self._embed(query).reshape(1, -1)
\n similarities = cosine_similarity(
\n query_embedding, self._example_embeddings
\n )[0]<\/p>\n
# MMR (Maximal Marginal Relevance) \u5e73\u8861\u76f8\u4f3c\u5ea6\u548c\u591a\u6837\u6027
\n selected_indices = []
\n remaining = list(range(len(self.examples)))<\/p>\n
for _ in range(min(k, len(self.examples))):
\n if not selected_indices:
\n # \u7b2c\u4e00\u4e2a\u9009\u6700\u76f8\u4f3c\u7684
\n best_idx = remaining[int(np.argmax(similarities[remaining]))]
\n else:
\n # \u540e\u7eed\u7528MMR\u516c\u5f0f：\u03bb*\u76f8\u4f3c\u5ea6 – (1-\u03bb)*\u5df2\u9009\u793a\u4f8b\u6700\u5927\u76f8\u4f3c\u5ea6
\n mmr_scores = []
\n for idx in remaining:
\n relevance = similarities[idx]
\n diversity = max(
\n cosine_similarity(
\n self._example_embeddings[idx].reshape(1, -1),
\n self._example_embeddings[selected_indices]
\n )[0]
\n ) if selected_indices else 0
\n mmr = (1 – diversity_weight) * relevance – diversity_weight * diversity
\n mmr_scores.append(mmr)
\n best_idx = remaining[int(np.argmax(mmr_scores))]<\/p>\n
selected_indices.append(best_idx)
\n remaining.remove(best_idx)<\/p>\n
return [self.examples[i] for i in selected_indices]<\/p>\n
# ===== \u4f7f\u7528\u6f14\u793a =====
\nexamples = [
\n {"input": "\u8fd9\u4e2a\u9910\u5385\u670d\u52a1\u6001\u5ea6\u5f88\u5dee", "output": "\u8d1f\u9762"},
\n {"input": "\u83dc\u54c1\u5473\u9053\u975e\u5e38\u68d2！", "output": "\u6b63\u9762"},
\n {"input": "\u73af\u5883\u4e00\u822c，\u4ef7\u683c\u8fd8\u884c", "output": "\u4e2d\u6027"},
\n {"input": "\u592a\u96be\u5403\u4e86，\u518d\u4e5f\u4e0d\u4f1a\u6765\u4e86", "output": "\u8d1f\u9762"},
\n {"input": "\u6027\u4ef7\u6bd4\u8d85\u9ad8，\u63a8\u8350！", "output": "\u6b63\u9762"},
\n {"input": "\u5916\u5356\u9001\u5f97\u5f88\u5feb\u4f46\u6709\u70b9\u51c9\u4e86", "output": "\u4e2d\u6027"},
\n]<\/p>\n
selector = FewShotSelector(examples)
\nselected = selector.select("\u8fd9\u5bb6\u5e97\u53e3\u5473\u7edd\u4e86，\u4e0b\u6b21\u8fd8\u6765", k=2)
\nfor i, ex in enumerate(selected, 1):
\n print(f"\u793a\u4f8b{i}: {ex['input']} \u2192 {ex['output']}")<\/p>\n
Prompt\u662f\u548c\u4ee3\u7801\u4e00\u6837\u91cd\u8981\u7684\u8d44\u4ea7\u3002\u4e0d\u7ba1\u7406Prompt\u7248\u672c = \u4e0d\u7ba1\u7406\u4ee3\u7801\u7248\u672c = \u7b49\u7740\u51fa\u4e8b\u6545\u3002<\/p>\n
"""
\nPrompt\u7248\u672c\u7ba1\u7406\u7cfb\u7edf
\n\u76ee\u5f55\u7ed3\u6784：
\nprompts\/
\n\u251c\u2500\u2500 code_review\/
\n\u2502 \u251c\u2500\u2500 v1.0.0.yaml
\n\u2502 \u251c\u2500\u2500 v1.1.0.yaml
\n\u2502 \u2514\u2500\u2500 v2.0.0.yaml \u2190 \u5f53\u524d\u751f\u4ea7\u7248\u672c
\n\u251c\u2500\u2500 text_analysis\/
\n\u2502 \u2514\u2500\u2500 v1.0.0.yaml
\n\u2514\u2500\u2500 changelog.md
\n"""<\/p>\n
import yaml
\nimport hashlib
\nfrom datetime import datetime
\nfrom pathlib import Path<\/p>\n
class PromptVersionManager:
\n """\u57fa\u4e8e\u6587\u4ef6\u7cfb\u7edf\u7684Prompt\u7248\u672c\u7ba1\u7406"""<\/p>\n
def __init__(self, base_path: str = ".\/prompts"):
\n self.base_path = Path(base_path)
\n self.base_path.mkdir(parents=True, exist_ok=True)<\/p>\n
def save(self, template: PromptTemplate, author: str = "system",
\n changelog: str = "") -> str:
\n """\u4fdd\u5b58\u65b0\u7248\u672cPrompt，\u81ea\u52a8\u751f\u6210\u7248\u672c\u53f7"""
\n prompt_dir = self.base_path \/ template.name
\n prompt_dir.mkdir(exist_ok=True)<\/p>\n
# \u81ea\u52a8\u9012\u589e\u7248\u672c\u53f7
\n existing = sorted(prompt_dir.glob("v*.yaml"))
\n if existing:
\n last_ver = existing[-1].stem.lstrip("v")
\n major, minor, patch = map(int, last_ver.split("."))
\n new_version = f"v{major}.{minor}.{patch + 1}"
\n else:
\n new_version = "v0.1.0"<\/p>\n
# \u5e8f\u5217\u5316Prompt\u6a21\u677f
\n data = {
\n "name": template.name,
\n "version": new_version,
\n "role": template.role,
\n "task": template.task,
\n "format_spec": template.format_spec,
\n "constraints": template.constraints,
\n "examples": template.examples,
\n "metadata": {
\n "author": author,
\n "created_at": datetime.now().isoformat(),
\n "content_hash": hashlib.sha256(
\n template.task.encode()
\n ).hexdigest()[:8],
\n "changelog": changelog
\n }
\n }<\/p>\n
filepath = prompt_dir \/ f"{new_version}.yaml"
\n with open(filepath, "w", encoding="utf-8") as f:
\n yaml.dump(data, f, allow_unicode=True, sort_keys=False)<\/p>\n
# \u66f4\u65b0changelog
\n self._update_changelog(template.name, new_version, changelog)<\/p>\n
return new_version<\/p>\n
def load(self, name: str, version: str = "latest") -> PromptTemplate:
\n """\u52a0\u8f7d\u6307\u5b9a\u7248\u672c\u7684Prompt"""
\n prompt_dir = self.base_path \/ name<\/p>\n
if version == "latest":
\n versions = sorted(prompt_dir.glob("v*.yaml"))
\n if not versions:
\n raise FileNotFoundError(f"No versions for: {name}")
\n filepath = versions[-1]
\n else:
\n filepath = prompt_dir \/ f"{version}.yaml"<\/p>\n
with open(filepath, "r", encoding="utf-8") as f:
\n data = yaml.safe_load(f)<\/p>\n
return PromptTemplate(
\n name=data["name"],
\n role=data.get("role", ""),
\n task=data["task"],
\n format_spec=data.get("format_spec", ""),
\n constraints=data.get("constraints", []),
\n examples=data.get("examples", []),
\n version=data["version"]
\n )<\/p>\n
def diff(self, name: str, v1: str, v2: str) -> str:
\n """\u5bf9\u6bd4\u4e24\u4e2a\u7248\u672c\u5dee\u5f02"""
\n t1 = self.load(name, v1)
\n t2 = self.load(name, v2)<\/p>\n
diff_lines = [f"# Diff: {name} {v1} \u2192 {v2}"]<\/p>\n
if t1.task != t2.task:
\n diff_lines.append("\\\\n## \u4efb\u52a1\u53d8\u66f4")
\n diff_lines.append(f"- {t1.task[:50]}…")
\n diff_lines.append(f"+ {t2.task[:50]}…")<\/p>\n
if t1.constraints != t2.constraints:
\n diff_lines.append("\\\\n## \u7ea6\u675f\u53d8\u66f4")
\n removed = set(t1.constraints) – set(t2.constraints)
\n added = set(t2.constraints) – set(t1.constraints)
\n for r in removed:
\n diff_lines.append(f"- [\u5220\u9664] {r}")
\n for a in added:
\n diff_lines.append(f"+ [\u65b0\u589e] {a}")<\/p>\n
return "\\\\n".join(diff_lines)<\/p>\n
def _update_changelog(self, name: str, version: str, message: str):
\n changelog_path = self.base_path \/ "changelog.md"
\n with open(changelog_path, "a", encoding="utf-8") as f:
\n f.write(
\n f"\\\\n## [{version}] {name} – "
\n f"{datetime.now().strftime('%Y-%m-%d')}\\\\n"
\n f"{message}\\\\n"
\n )<\/p>\n
\u914d\u4e0agit，\u8fd9\u5c31\u662f\u4e00\u5957\u5b8c\u6574\u7684Prompt CI\/CD\u3002\u6bcf\u6b21\u6539Prompt\u90fd\u8d70MR \u2192 Review \u2192 \u5408\u5e76 \u2192 \u90e8\u7f72\u7684\u6807\u51c6\u6d41\u7a0b\u3002Prompt\u4e0a\u7ebf\u540e\u6548\u679c\u53d8\u5dee？git revert \u56de\u6eda\u5230\u4e0a\u4e00\u4e2a\u7248\u672c，\u8ddf\u56de\u6eda\u4ee3\u7801\u4e00\u6837\u7b80\u5355\u3002<\/p>\n
\u4f60\u600e\u4e48\u77e5\u9053\u65b0Prompt\u771f\u7684\u6bd4\u65e7\u7684\u597d？\u51ed\u611f\u89c9\u5417？\u4e0d\u884c\u2014\u2014\u5f97\u8dd1\u5b9e\u9a8c\u3002<\/p>\n
import time
\nfrom collections import defaultdict
\nfrom statistics import mean, stdev<\/p>\n
class PromptABTest:
\n """Prompt A\/B\u6d4b\u8bd5\u6846\u67b6"""<\/p>\n
def __init__(self, model_func):
\n self.model_func = model_func
\n self.results: dict[str, list[dict]] = defaultdict(list)<\/p>\n
def run(self, variant: str, prompt: str,
\n test_cases: list[dict], metrics: list[str] = None):
\n """
\n \u8fd0\u884c\u6d4b\u8bd5<\/p>\n
Args:
\n variant: "A" \u6216 "B"
\n prompt: Prompt\u6a21\u677f（\u542b{input}\u5360\u4f4d\u7b26）
\n test_cases: [{"input": …, "expected": …}]
\n metrics: \u9700\u8981\u8ba1\u7b97\u7684\u6307\u6807\u5217\u8868
\n """
\n for case in test_cases:
\n start = time.time()
\n full_prompt = prompt.format(input=case["input"])
\n output = self.model_func(full_prompt)
\n elapsed = time.time() – start<\/p>\n
self.results[variant].append({
\n "input": case["input"],
\n "expected": case.get("expected", ""),
\n "output": output,
\n "latency": elapsed
\n })<\/p>\n
def compare(self, metrics: dict = None) -> dict:
\n """\u5bf9\u6bd4A\/B\u4e24\u7ec4\u7684\u5dee\u5f02"""
\n if metrics is None:
\n metrics = {"latency": lambda r: r["latency"]}<\/p>\n
comparison = {}
\n for metric_name, metric_fn in metrics.items():
\n vals_a = [metric_fn(r) for r in self.results["A"]]
\n vals_b = [metric_fn(r) for r in self.results["B"]]<\/p>\n
comparison[metric_name] = {
\n "A_mean": mean(vals_a),
\n "B_mean": mean(vals_b),
\n "A_std": stdev(vals_a) if len(vals_a) > 1 else 0,
\n "B_std": stdev(vals_b) if len(vals_b) > 1 else 0,
\n "delta": mean(vals_b) – mean(vals_a),
\n "delta_pct": (mean(vals_b) – mean(vals_a))
\n \/ mean(vals_a) * 100 if mean(vals_a) != 0 else 0
\n }<\/p>\n
return comparison<\/p>\n
# ===== \u4f7f\u7528\u793a\u4f8b =====
\ndef mock_model(prompt: str) -> str:
\n """\u6a21\u62df\u6a21\u578b\u8c03\u7528（\u66ff\u6362\u4e3a\u771f\u5b9e\u7684API\u8c03\u7528）"""
\n import random
\n return f"Mock output for: {prompt[:30]}… (score: {random.randint(60,100)})"<\/p>\n
ab_test = PromptABTest(mock_model)<\/p>\n
test_cases = [
\n {"input": "\u8fd9\u4e2a\u4ea7\u54c1\u600e\u4e48\u9000\u6362\u8d27？", "expected": "\u5305\u542b\u9000\u6362\u8d27\u6d41\u7a0b"},
\n {"input": "\u7269\u6d41\u5230\u54ea\u91cc\u4e86？", "expected": "\u5305\u542b\u7269\u6d41\u67e5\u8be2\u65b9\u5f0f"},
\n {"input": "\u4e0b\u5355\u540e\u591a\u4e45\u53d1\u8d27？", "expected": "\u5305\u542b\u53d1\u8d27\u65f6\u95f4\u627f\u8bfa"},
\n]<\/p>\n
# \u7248\u672cA：\u7b80\u5355Prompt
\nab_test.run("A", "\u56de\u7b54\u95ee\u9898：{input}", test_cases)<\/p>\n
# \u7248\u672cB：\u7ed3\u6784\u5316Prompt
\nab_test.run("B", """
\n\u4f60\u662f\u4e13\u4e1a\u5ba2\u670d\u3002\u8bf7\u6309\u4ee5\u4e0b\u683c\u5f0f\u56de\u7b54：
\n\u95ee\u9898：{input}
\n\u7b54\u6848（50\u5b57\u4ee5\u5185）：
\n""", test_cases)<\/p>\n
result = ab_test.compare()
\nprint(f"A\/B\u6d4b\u8bd5\u7ed3\u679c：\\\\n{json.dumps(result, ensure_ascii=False, indent=2)}")<\/p>\n
\u771f\u6b63\u7684A\/B\u6d4b\u8bd5\u6ca1\u90a3\u4e48\u7b80\u5355\u2014\u2014\u4f60\u9700\u8981\u5b9a\u4e49"\u597d"\u7684\u6807\u51c6\u3002\u7535\u5546\u5ba2\u670d\u573a\u666f\u91cc，\u201c\u597d"\u53ef\u80fd\u662f"\u7528\u6237\u4e0d\u518d\u8ffd\u95ee\u201d；\u4ee3\u7801\u751f\u6210\u573a\u666f\u91cc，\u201c\u597d"\u53ef\u80fd\u662f"\u4e00\u6b21\u8fc7\u7f16\u8bd1\u201d\u3002\u5148\u5b9a\u4e49\u6307\u6807，\u518d\u8dd1\u5b9e\u9a8c，\u5426\u5219\u6570\u636e\u6beb\u65e0\u610f\u4e49\u3002<\/p>\n
\u8fd9\u662f\u8001\u677f\u6700\u5173\u5fc3\u4f46\u4f60\u6700\u5bb9\u6613\u5ffd\u7565\u7684\u95ee\u9898\u3002<\/p>\n
graph TD
\n Q["\u8f93\u5165\u4efb\u52a1"] –> C{"\u590d\u6742\u5ea6\u5206\u7c7b\u5668"}<\/p>\n
C –>|"\u7b80\u5355\u4efb\u52a1<br\/>(\u7ffb\u8bd1\/\u63d0\u53d6\/\u5206\u7c7b)"| S["Short Prompt<br\/>L1-L2 \u7ea7\u522b<br\/>Token: ~500"]
\n C –>|"\u4e2d\u7b49\u4efb\u52a1<br\/>(\u603b\u7ed3\/\u6539\u5199)"| M["Medium Prompt<br\/>L2-L3 \u7ea7\u522b<br\/>Token: ~1500"]
\n C –>|"\u590d\u6742\u4efb\u52a1<br\/>(\u63a8\u7406\/\u4ee3\u7801\/\u5206\u6790)"| L["Long Prompt<br\/>L3-L4 \u7ea7\u522b<br\/>Token: ~4000"]<\/p>\n
S –> S_C["\u5355\u6b21\u8c03\u7528<br\/>💰 \u6210\u672c: $0.002"]
\n M –> M_C["2-3\u6b21\u8c03\u7528<br\/>💰 \u6210\u672c: $0.01"]
\n L –> L_C["3-5\u6b21\u8c03\u7528+\u8bc4\u5ba1<br\/>💰 \u6210\u672c: $0.05"]<\/p>\n
style S fill:#90EE90
\n style M fill:#FFD700
\n style L fill:#FF6347<\/p>\n
\u6838\u5fc3\u51b3\u7b56\u6846\u67b6\u2014\u2014\u6309\u4efb\u52a1\u4ef7\u503c\u5339\u914dPrompt\u7ea7\u522b：<\/p>\n
from enum import Enum<\/p>\n
class TaskTier(Enum):
\n """\u4efb\u52a1\u7ea7\u522b\u2014\u2014\u51b3\u5b9a\u4e86\u4f60\u613f\u610f\u82b1\u591a\u5c11\u94b1"""
\n COST_SENSITIVE = 1 # \u9ad8\u9891\u3001\u4f4e\u4ef7\u503c（\u5982\u6279\u91cf\u5206\u7c7b）
\n STANDARD = 2 # \u5e38\u89c4\u4e1a\u52a1
\n QUALITY_CRITICAL = 3 # \u6838\u5fc3\u4e1a\u52a1\u3001\u4e0d\u53ef\u51fa\u9519<\/p>\n
class CostQualityOptimizer:
\n """\u6210\u672c-\u8d28\u91cf\u51b3\u7b56\u5668"""<\/p>\n
# \u5404\u7ea7\u522b\u7684Prompt\u914d\u7f6e
\n TIER_CONFIG = {
\n TaskTier.COST_SENSITIVE: {
\n "max_tokens": 500,
\n "cot_enabled": False,
\n "self_reflection": False,
\n "model": "gpt-3.5-turbo",
\n "estimated_cost_per_1k": 0.001
\n },
\n TaskTier.STANDARD: {
\n "max_tokens": 1500,
\n "cot_enabled": True,
\n "self_reflection": False,
\n "model": "gpt-4o-mini",
\n "estimated_cost_per_1k": 0.003
\n },
\n TaskTier.QUALITY_CRITICAL: {
\n "max_tokens": 4000,
\n "cot_enabled": True,
\n "self_reflection": True,
\n "model": "gpt-4o",
\n "estimated_cost_per_1k": 0.03
\n }
\n }<\/p>\n
@classmethod
\n def optimize(cls, task: str, tier: TaskTier,
\n expected_calls_per_day: int = 1000) -> dict:
\n """
\n \u6839\u636e\u4efb\u52a1\u7ea7\u522b\u9009\u62e9\u6700\u4f18Prompt\u7b56\u7565<\/p>\n
Returns:
\n {
\n "strategy": Prompt\u7b56\u7565\u8bf4\u660e,
\n "estimated_daily_cost": \u9884\u4f30\u65e5\u6210\u672c,
\n "prompt_template": \u63a8\u8350\u7684Prompt\u6a21\u677f
\n }
\n """
\n config = cls.TIER_CONFIG[tier]
\n tokens_per_call = config["max_tokens"]
\n cost_per_call = (tokens_per_call \/ 1000) * config["estimated_cost_per_1k"]<\/p>\n
# \u81ea\u53cd\u601d\u989d\u5916\u6210\u672c
\n if config["self_reflection"]:
\n cost_per_call *= 3 # \u751f\u6210+\u8bc4\u5ba1+\u4fee\u6b63<\/p>\n
daily_cost = cost_per_call * expected_calls_per_day<\/p>\n
return {
\n "tier": tier.name,
\n "model": config["model"],
\n "cot": config["cot_enabled"],
\n "self_reflection": config["self_reflection"],
\n "max_tokens_per_call": tokens_per_call,
\n "cost_per_call": round(cost_per_call, 5),
\n "estimated_daily_cost": round(daily_cost, 2),
\n "estimated_monthly_cost": round(daily_cost * 30, 2),
\n "strategy": cls._build_strategy_text(config)
\n }<\/p>\n
@classmethod
\n def _build_strategy_text(cls, config: dict) -> str:
\n parts = []
\n parts.append(f"\u6a21\u578b: {config['model']}")
\n parts.append(
\n "CoT: " + ("\u542f\u7528" if config['cot_enabled'] else "\u7981\u7528"))
\n parts.append(
\n "\u81ea\u53cd\u601d: " + ("\u542f\u7528" if config['self_reflection'] else "\u7981\u7528"))
\n return " | ".join(parts)<\/p>\n
# ===== \u5b9e\u9645\u51b3\u7b56\u793a\u4f8b =====
\n# \u573a\u666f：\u6bcf\u65e510\u4e07\u6761\u5546\u54c1\u8bc4\u8bba\u60c5\u611f\u5206\u7c7b
\nresult = CostQualityOptimizer.optimize(
\n task="\u5546\u54c1\u8bc4\u8bba\u60c5\u611f\u5206\u7c7b",
\n tier=TaskTier.COST_SENSITIVE,
\n expected_calls_per_day=100000
\n)
\nprint(f"""
\n\u6210\u672c\u5206\u6790：
\n– \u7b56\u7565：{result['strategy']}
\n– \u5355\u6b21\u8c03\u7528\u6210\u672c：${result['cost_per_call']}
\n– \u9884\u4f30\u65e5\u6210\u672c：${result['estimated_daily_cost']}
\n– \u9884\u4f30\u6708\u6210\u672c：${result['estimated_monthly_cost']}<\/p>\n
💡 \u5efa\u8bae：10\u4e07\u6761\/\u5929\u7684\u6279\u91cf\u5206\u7c7b，\u7528GPT-3.5-turbo + L1\u7ea7\u522bPrompt\u5c31\u591f\u4e86\u3002
\n\u5982\u679c\u6362\u6210GPT-4o + \u5b8c\u6574CoT + \u81ea\u53cd\u601d，\u6708\u6210\u672c\u4ece$3,000\u98d9\u5347\u5230$90,000\u3002
\n\u8d28\u91cf\u63d0\u53475%，\u6210\u672c\u589e\u52a030\u500d\u2014\u2014\u503c\u4e0d\u503c？\u4f60\u81ea\u5df1\u5224\u65ad\u3002
\n""")<\/p>\n
\u56de\u987e\u4e00\u4e0b，\u6211\u4eec\u4ece"\u628a\u8bdd\u8bf4\u6e05\u695a"\u8d70\u5230"\u8ba9AI\u81ea\u5df1\u5ba1\u81ea\u5df1"，\u8fd9\u662f\u6211\u4ece\u51e0\u5341\u4e2a\u9879\u76ee\u91cc\u8e29\u5751\u8e29\u51fa\u6765\u7684\u4f53\u7cfb\u3002\u4f46\u8fd9\u5957\u4f53\u7cfb\u6700\u597d\u7684\u5b66\u4e60\u65b9\u5f0f\u4e0d\u662f\u8bfb\u5b8c\u8fd9\u7bc7\u6587\u7ae0\u2014\u2014\u800c\u662f\u6253\u5f00Colab，\u62ff\u4e00\u4e2a\u4f60\u5de5\u4f5c\u4e2d\u7684\u771f\u5b9e\u4efb\u52a1，\u4eceL1\u5f00\u59cb\u4e00\u5c42\u4e00\u5c42\u5f80\u4e0a\u52a0，\u5bf9\u6bd4\u6bcf\u4e00\u5c42\u7684\u8f93\u51fa\u8d28\u91cf\u53d8\u5316\u3002<\/p>\n
\u4f60\u4eb2\u773c\u770b\u5230\u7684\u53d8\u5316，\u6bd4\u8bfb\u5341\u7bc7\u6587\u7ae0\u90fd\u6df1\u523b\u3002<\/p>\n
AI\u4e13\u4e1a\u4ea4\u6d41，\u4fe1\u606f\u5171\u4eab，\u4e0d\u6784\u6210\u6295\u8d44\u5efa\u8bae\u3002\u6587\u4e2d\u4ee3\u7801\u5747\u4e3a\u793a\u4f8b，\u4f7f\u7528\u524d\u8bf7\u5145\u5206\u6d4b\u8bd5\u3002 \u7b14\u8005\u4e0e\u6587\u4e2d\u63d0\u5230\u7684OpenAI\u3001Anthropic\u7b49\u516c\u53f8\u65e0\u5229\u76ca\u5173\u7cfb\u3002<\/p>\n
\u5173\u6ce8\u6211，\u540e\u53f0\u56de\u590d \u201cPrompt\u201d，\u514d\u8d39\u83b7\u53d6：<\/p>\n
\u4e0b\u4e00\u7bc7：\u300aAI\u9879\u76ee\u8bc4\u4f30\u2014\u2014\u4ece\u51c6\u786e\u7387\u5230\u7528\u6237\u6ee1\u610f\u5ea6\u7684\u591a\u7ef4\u5ea6\u5ea6\u91cf\u300b<\/p>\n
\u522b\u518d\u7528"\u51c6\u786e\u7387"\u7cca\u5f04\u8001\u677f\u4e86\u3002\u4f60\u4f1a\u53d1\u73b0：<\/p>\n
\u4e0b\u4e00\u7bc7\u89c1\u3002<\/p>\n
\u6807\u7b7e： Prompt Engineering\u3001LLM\u3001\u601d\u7ef4\u94fe\u3001Few-shot\u3001\u6a21\u677f\u8bbe\u8ba1\u3001AI\u5f00\u53d1\u3001GPT<\/p>\n","protected":false},"excerpt":{"rendered":"
\u76ee\u5f55
\n\u4e00\u3001Prompt Engineering\u56db\u5c42\u4f53\u7cfb
\n\u7b2c\u4e00\u5c42：\u660e\u786e\u6307\u4ee4\u2014\u2014\u628a\u8bdd\u8bf4\u6e05\u695a
\n\u7b2c\u4e8c\u5c42：\u63d0\u4f9b\u4e0a\u4e0b\u6587\u2014\u2014\u7ed9AI\u5b89\u88c5\\”\u8bb0\u5fc6\u82af\u7247\\”
\n\u7b2c\u4e09\u5c42：\u601d\u7ef4\u94fe(CoT)\u2014\u2014\u6559AI\u4e00\u6b65\u4e00\u6b65\u60f3
\n\u7b2c\u56db\u5c42：\u81ea\u6211\u53cd\u601d\u8f93\u51fa\u6821\u9a8c\u2014\u2014\u8ba9AI\u81ea\u5df1\u627e\u9519\u8bef
\n\u4e8c\u3001Prompt\u6a21\u677f\u4f53\u7cfb\u8bbe\u8ba1
\n\u4e09\u3001\u7ed3\u6784\u5316\u8f93\u51fa\u63a7\u5236
\n\u56db\u3001Few-shot\u793a\u4f8b\u9009\u62e9\u7b56\u7565
\n\u4e94\u3001Prompt\u7248\u672c\u7ba1\u7406
\n\u516d\u3001A\/B\u6d4b\u8bd5：Prompt\u7684\\”\u7070\u5ea6\u53d1\u5e03\\”
\n\u4e03\u3001\u6210\u672c<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[8158,10369,75,2946,50],"topic":[],"class_list":["post-90972","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-10369","tag-llm","tag-prompt","tag-50"],"yoast_head":"\n