\u56e0\u6b64\uff0c\u4ece\u8bbe\u8ba1\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u8981\u60f3\u53d1\u6325 ChatGPT \u7684\u6700\u5927\u80fd\u529b\uff0c\u4e0d\u4ec5\u4ec5\u8981\u9760\u5b83\u7684 In-Context \u80fd\u529b\uff0cPrompt \u4e5f\u5e94\u8be5\u4ed4\u7ec6\u8bbe\u8ba1\uff0c\u6216\u8005\u8bf4\u5c3d\u91cf\u8d34\u8fd1\u8bad\u7ec3\u65f6\u7684\u6837\u5b50\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5b83\u662f\u9047\u5f3a\u5219\u5f3a\uff0c\u9047\u5f31\u5219\u5f31\uff0c\u9047\u50bb\u903c\u5219\u50bb\u903c\u3002\u3002\u3002<\/p>\n

Prompt\u8bbe\u8ba1<\/strong><\/span><\/h2>\n

ChatGPT \u6709\u4e0d\u5c11\u7279\u70b9\uff0c\u6bd4\u5982\uff1a\u8de8\u8bed\u8a00\u3001\u540c\u65f6\u670d\u52a1\u591a\u4eba\uff08\u4f38\u7f29\u6027\uff09\u3001\u4e2a\u6027\u5316\uff08\u5229\u7528\u5386\u53f2\u8bb0\u5f55\uff09\u7b49\u3002\u4f46\u6700\u503c\u5f97\u4e00\u63d0\u7684\u662f\u5b9a\u5236\u5316\uff0c\u4e5f\u5c31\u662f\u53ef\u4ee5\u5b9a\u5236\u8bed\u6c14\u3001\u98ce\u683c\u3001\u7c7b\u578b\u7b49\uff0c\u8fd9\u4e2a\u4e5f\u53ef\u4ee5\u5305\u542b\u5728\u4f60\u7684 Prompt \u91cc\u3002<\/p>\n

Prompt\u539f\u5219<\/strong><\/span><\/h4>\n

\u9996\u5148\u8981\u8bf4\u7684\u662f\u8bbe\u8ba1\u539f\u5219\uff0c\u4e3b\u8981\u5305\u542b\u4ee5\u4e0b\u51e0\u4e2a\uff1a<\/p>\n

\n

\n
• \n
  \u6e05\u6670\uff0c\u5207\u5fcc\u590d\u6742\u6216\u6b67\u4e49\uff0c\u5982\u679c\u6709\u672f\u8bed\uff0c\u5e94\u5b9a\u4e49\u6e05\u695a\u3002<\/section>\n<\/li>\n

  \n

  \u5177\u4f53\uff0c\u63cf\u8ff0\u8bed\u8a00\u5e94\u5c3d\u91cf\u5177\u4f53\uff0c\u4e0d\u8981\u62bd\u8c61\u6d3b\u6a21\u68f1\u4e24\u53ef\u3002<\/section>\n<\/li>\n

  \n

  \u805a\u7126\uff0c\u95ee\u9898\u907f\u514d\u592a\u6cdb\u6216\u5f00\u653e\u3002<\/section>\n<\/li>\n

  \n

  \u7b80\u6d01\uff0c\u907f\u514d\u4e0d\u5fc5\u8981\u7684\u63cf\u8ff0\u3002<\/section>\n<\/li>\n

  \n

  \u76f8\u5173\uff0c\u4e3b\u8981\u6307\u4e3b\u9898\u76f8\u5173\uff0c\u800c\u4e14\u662f\u6574\u4e2a\u5bf9\u8bdd\u671f\u95f4\uff0c\u4e0d\u8981\u4e1c\u4e00\u74e2\u897f\u4e00\u74e4\u3002<\/section>\n<\/li>\n<\/ul>\n<\/section>\n

  \u4e3e\u51e0\u4e2a\u3010\u6838\u5fc3\u6587\u732e1\u3011\u4e2d\u7684\u4f8b\u5b50\uff08\u6362\u6210\u4e86\u4e2d\u6587\uff09\uff1a<\/p>\n

  # \u6709\u6548 Prompt\u4f60\u80fd\u603b\u7ed3\u4e00\u4e0b\u300a\u953b\u70bc\u7684\u597d\u5904\u300b\u4e00\u6587\u7684\u8981\u70b9\u5417\uff1f  # \u805a\u7126\u3001\u76f8\u5173\u5df4\u9ece\u6700\u597d\u7684\u7d20\u98df\u9910\u5385\u6709\u54ea\u4e9b\uff1f  # \u5177\u4f53\u3001\u76f8\u5173# \u65e0\u6548 Prompt\u4f60\u80fd\u544a\u8bc9\u6211\u5173\u4e8e\u8fd9\u4e2a\u4e16\u754c\u7684\u4ec0\u4e48\uff1f  # \u5bbd\u6cdb\u3001\u5f00\u653e\u4f60\u80fd\u5e2e\u6211\u505a\u4f5c\u4e1a\u5417\uff1f  # \u5f00\u653e\u4f60\u597d  # \u65e0\u76ee\u7684\u3001\u4e0d\u805a\u7126<\/pre>\n
  \u5f53\u7136\uff0c\u8fd9\u51e0\u4e2a\u4f8b\u5b50\u662f\u7ad9\u5728\u300c\u4f60\u8981\u83b7\u5f97\u6709\u6548\u4fe1\u606f\u300d\u7684\u57fa\u7840\u4e0a\u8bf4\u7684\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u8fd9\u4e9b\u539f\u5219\u662f\u9488\u5bf9\u4f60\u60f3\u8981\u901a\u8fc7 ChatGPT \u83b7\u53d6\u5230\u6709\u7528\u4fe1\u606f\u6216\u5b8c\u6210\u7279\u5b9a\u4efb\u52a1\u3002\u629b\u5f00\u8fd9\u4e2a\u524d\u63d0\uff0c\u4e00\u4e9b\u5f00\u653e\u3001\u5bbd\u6cdb\u7684\u95ee\u9898\u4e5f\u65e0\u53ef\u539a\u975e\u3002\u4e00\u65b9\u9762\uff0c\u53ef\u4ee5\u4e86\u89e3\u6a21\u578b\u5728\u8fd9\u7c7b\u95ee\u9898\u4e0a\u7684\u80fd\u529b\uff1b\u53e6\u4e00\u65b9\u9762\uff0cChatGPT \u672c\u8eab\u5e76\u6ca1\u6709\u89c4\u5b9a\u4f60\u5fc5\u987b\u95ee\u4ec0\u4e48\u4e0d\u95ee\u4ec0\u4e48\uff0c\u8fd9\u79cd\u95ee\u9898\u4e5f\u662f\u4e00\u79cd\u4fe1\u606f\u3002<\/p>\n
  \u4e8b\u5b9e\u4e0a\uff0cChatGPT\uff08\u6216\u8005\u8bf4\uff0c\u4e00\u4e2a\u6709\u8ffd\u6c42\u7684\u673a\u5668\u4eba\uff09\u4e5f\u662f\u5e0c\u671b\u6211\u4eec\u628a\u5b83\u5f53\u505a\u5408\u4f5c\u4f19\u4f34\uff08\u52a9\u7406\uff09\u3001\u5bfc\u5e08\uff08\u6559\u80b2\uff09\u3001\u670b\u53cb\uff08\u804a\u5929\u3001\u60c5\u611f\uff09\u3001\u767e\u79d1\u5168\u4e66\uff08\u4fe1\u606f\u83b7\u53d6\uff09\u3002\u6211\u4eec\u751a\u81f3\u53ef\u4ee5\u60f3\u8c61\uff0c\u4ee5\u540e\u4e00\u5b9a\u4f1a\u51fa\u73b0\u7c7b\u4f3c\u79d1\u5e7b\u7535\u5f71\u300a\u4eba\u5de5\u667a\u80fd\u300b\u4e2d\u90a3\u4e2a\u4e07\u4e8b\u901a\u535a\u58eb\uff08\u7535\u5f71 1 \u5206 26 \u79d2\uff09\uff0c\u6216\u8005\u79d1\u5e7b\u5c0f\u8bf4\u300aThe IWM 1000\u300b\u4e2d\u7684\u90a3\u4e2a IWM 1000 \u4eea\u5668\uff08\u867d\u7136\u8fd9\u4e2a\uff0c\u989d\uff0c\u6709\u70b9\u60b2\u89c2\u8272\u5f69\uff0c\u4f46\u90a3\u662f\u53e6\u4e00\u4e2a\u8bdd\u9898\u4e86\uff09\u3002\u5662\uff0c\u6211\u8fd8\u95ee\u4e86 ChatGPT \u5927\u536b\u7684\u95ee\u9898\uff0c\u6211\u8ba4\u4e3a\u4ed6\u56de\u7b54\u7684\u4e0d\u9519\uff0c\u611f\u5174\u8da3\u7684\u53ef\u4ee5\u770b\u540e\u9762\u300a\u9644\u5f55\u4e00\u300b\u3002<\/p>\n
   Prompt\u6b65\u9aa4<\/strong><\/p>\n
  \u4e00\u822c\u5305\u62ec\u4ee5\u4e0b\u6b65\u9aa4\u3010\u6838\u5fc3\u6587\u732e1\u3011\u3002<\/p>\n
  \u5bf9\u8bdd\u524d\uff1a<\/p>\n
  \n
  \n
  \n
  \u660e\u786e\u76ee\u7684\u5e76\u59cb\u7ec8\u805a\u7126\u76ee\u7684\u3002<\/section>\n<\/li>\n
  \n
  \u4f7f\u7528\u6e05\u6670\u3001\u5177\u4f53\u3001\u76f8\u5173\u7684\u8bed\u8a00\u7b80\u6d01\u5730\u63cf\u8ff0\u4f60\u7684\u76ee\u7684\u3002<\/section>\n<\/li>\n
  \n
  \u907f\u514d\u4f7f\u7528\u5f00\u653e\u5f0f\u6216\u8fc7\u4e8e\u5bbd\u6cdb\u7684 Prompt\u3002<\/section>\n<\/li>\n
  \n
  \u67e5\u770b\u548c\u4fee\u6539 Prompt\u3002<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u5bf9\u8bdd\u4e2d\uff1a<\/p>\n
  \n
  \n
  \n
  \u9f13\u52b1 ChatGPT \u6269\u5c55\u5185\u5bb9\u3002<\/section>\n<\/li>\n
  \n
  \u6ce8\u610f\u5bf9\u8bdd\u4e2d\u7684\u8bed\u6c14\u548c\u8bed\u8a00\u3002<\/section>\n<\/li>\n
  \n
  \u6ce8\u610f\u5bf9\u8bdd\u7684\u65b9\u5411\uff0c\u9002\u65f6\u505a\u51fa\u8c03\u6574\u3002<\/section>\n<\/li>\n
  \n
  \u5fc5\u8981\u65f6\u53ef\u4ee5\u4f7f\u7528\u300c\u89d2\u8272\u626e\u6f14\u300d\uff08\u5982\u300c\u5047\u8bbe\u4f60\u662fXXX\u300d\uff09\u5e2e\u52a9 ChatGPT \u7406\u89e3\u5b83\u7684\u89d2\u8272\u3002<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u5bf9\u8bdd\u540e\uff1a<\/p>\n
  \n
  \n
  \n
  \u56de\u987e\u6574\u901a\u5bf9\u8bdd\uff0c\u68c0\u67e5\u662f\u5426\u6709\u8fdd\u53cd\u539f\u5219\u6216\u5931\u8bef\u7684\u5730\u65b9\u3002<\/section>\n<\/li>\n
  \n
  \u6ce8\u610f\u4e0d\u540c Prompt \u4e0b ChatGPT \u7684\u53cd\u9988\uff0c\u4e86\u89e3\u5176\u80fd\u529b\u548c\u5c40\u9650\u3002<\/section>\n<\/li>\n
  \n
  \u68b3\u7406 Prompt \u5e76\u5728\u5fc5\u8981\u65f6\u91cd\u65b0\u6d4b\u8bd5\u3002<\/section>\n<\/li>\n
  \n
  \u5982\u679c\u95ee\u9898\u6ca1\u6709\u5f97\u5230\u89e3\u51b3\uff0c\u5c1d\u8bd5\u4f7f\u7528\u66f4\u591a\u4e0d\u540c\u79cd\u7c7b\u7684 Prompt \u8fdb\u884c\u6d4b\u8bd5\u3002<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u518d\u4e3e\u4e2a\u3010\u6838\u5fc3\u6587\u732e1\u3011\u4e2d\u7684\u4f8b\u5b50\uff0c\u4f9d\u7136\u6362\u6210\u4e2d\u6587\uff1a<\/p>\n
  \u6211\u5e0c\u671b\u4f60\u5145\u5f53 JavaScript console\u3002\u6211\u5c06\u952e\u5165\u547d\u4ee4\uff0c\u60a8\u5c06\u56de\u590d JavaScript console \u5e94\u663e\u793a\u7684\u5185\u5bb9\u3002\u6211\u5e0c\u671b\u4f60\u53ea\u56de\u590d\u4e00\u4e2a\u552f\u4e00\u4ee3\u7801\u5757\u4e2d\u7684\u7ec8\u7aef\u8f93\u51fa\uff0c\u6ca1\u6709\u522b\u7684\u3002\u4e0d\u8981\u5199\u6ce8\u91ca\u3002\u9664\u975e\u6211\u6307\u793a\u4f60\u8fd9\u6837\u505a\uff0c\u5426\u5219\u4e0d\u8981\u952e\u5165\u547d\u4ee4\u3002\u5f53\u6211\u9700\u8981\u7528\u82f1\u8bed\u544a\u8bc9\u4f60\u4e00\u4e9b\u4e8b\u60c5\u65f6\uff0c\u6211\u4f1a\u901a\u8fc7\u5c06\u6587\u672c\u653e\u5728\u5927\u62ec\u53f7\u5185{\u50cf\u8fd9\u6837}\u6765\u505a\u5230\u8fd9\u4e00\u70b9\u3002\u6211\u7684\u7b2c\u4e00\u4e2a\u547d\u4ee4\u662f console.log\uff08\u201cHello World\u201d\uff09;<\/pre>\n
  \u6765\u770b\u770b\u8fd9\u4e2a\u4f8b\u5b50\uff1a<\/p>\n
  \n
  \n
  \n
  \u201c\u6211\u5e0c\u671b\u4f60\u5145\u5f53 JavaScript console\u3002\u201d\u8fd9\u53e5\u8bdd\u4f7f\u7528\u4e86\u300c\u5145\u5f53XX\u300d\u8fd9\u6837\u7684 Prompt \u544a\u8bc9 ChatGPT \u7684\u89d2\u8272\u3002<\/section>\n<\/li>\n
  \n
  \u201c\u6211\u5c06\u952e\u5165\u547d\u4ee4\uff0c\u60a8\u5c06\u56de\u590d JavaScript console \u5e94\u663e\u793a\u7684\u5185\u5bb9\u3002\u201d\u8fd9\u53e5\u8bdd\u89e3\u91ca\u4e86\u7528\u6237\u7684\u89d2\u8272\uff0c\u4ee5\u53ca ChatGPT \u54cd\u5e94\u7528\u6237\u547d\u4ee4\u65f6\u7684\u89d2\u8272\u3002<\/section>\n<\/li>\n
  \n
  \u201c\u6211\u5e0c\u671b\u4f60\u53ea\u56de\u590d\u4e00\u4e2a\u552f\u4e00\u4ee3\u7801\u5757\u4e2d\u7684\u7ec8\u7aef\u8f93\u51fa\uff0c\u6ca1\u6709\u522b\u7684\u3002\u201d\u8fd9\u53e5\u8bdd\u4e3a ChatGPT \u63d0\u4f9b\u4e86\u8fdb\u4e00\u6b65\u7684\u8bf4\u660e\uff0c\u6307\u5b9a\u5b83\u5e94\u8be5\u53ea\u5728\u4e00\u4e2a\u552f\u4e00\u7684\u4ee3\u7801\u5757\u5185\u4f7f\u7528\u7ec8\u7aef\u8f93\u51fa\u8fdb\u884c\u56de\u590d\uff0c\u5e76\u4e14\u5728\u5176\u56de\u590d\u4e2d\u4e0d\u5305\u542b\u4efb\u4f55\u5176\u4ed6\u5185\u5bb9\u6216\u89e3\u91ca\u3002<\/section>\n<\/li>\n
  \n
  \u201c\u4e0d\u8981\u5199\u6ce8\u91ca\u3002\u201d\u8fd9\u53e5\u8bdd\u662f\u5bf9\u4e0a\u4e00\u53e5\u6307\u4ee4\u7684\u91cd\u590d\uff0c\u5f3a\u8c03 ChatGPT \u4e0d\u5e94\u5728\u5176\u54cd\u5e94\u4e2d\u5199\u4efb\u4f55\u6ce8\u91ca\u3002<\/section>\n<\/li>\n
  \n
  \u201c\u9664\u975e\u6211\u6307\u793a\u4f60\u8fd9\u6837\u505a\uff0c\u5426\u5219\u4e0d\u8981\u8f93\u5165\u547d\u4ee4\u3002\u201d\u8fd9\u53e5\u8bdd\u4e3a ChatGPT \u63d0\u4f9b\u4e86\u8fdb\u4e00\u6b65\u7684\u8bf4\u660e\uff0c\u6307\u5b9a\u5b83\u4e0d\u5e94\u952e\u5165\u4efb\u4f55\u547d\u4ee4\uff0c\u9664\u975e\u7528\u6237\u6307\u793a\u8fd9\u6837\u505a\u3002<\/section>\n<\/li>\n
  \n
  \u201c\u5f53\u6211\u9700\u8981\u7528\u82f1\u8bed\u544a\u8bc9\u4f60\u4e00\u4e9b\u4e8b\u60c5\u65f6\uff0c\u6211\u4f1a\u901a\u8fc7\u5c06\u6587\u672c\u653e\u5728\u5927\u62ec\u53f7\u5185{\u50cf\u8fd9\u6837}\u6765\u505a\u5230\u8fd9\u4e00\u70b9\u3002\u201d\u8fd9\u53e5\u8bdd\u901a\u8fc7\u5c06\u6587\u672c\u62ec\u5728\u5927\u62ec\u53f7\u4e2d\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u5982\u4f55\u7528\u82f1\u8bed\u4e0e ChatGPT \u8fdb\u884c\u4ea4\u6d41\u7684\u8bf4\u660e\u3002<\/section>\n<\/li>\n
  \n
  \u201c\u6211\u7684\u7b2c\u4e00\u4e2a\u547d\u4ee4\u662f console.log(“Hello World”);\u201d\u8fd9\u53e5\u8bdd\u63d0\u4f9b\u4e86\u63d0\u793a\u7b26\u7684\u7b2c\u4e00\u4e2a\u547d\u4ee4\uff0c\u56e0\u6b64 ChatGPT \u5c06\u9996\u5148\u8fd0\u884c\u3002<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u7b80\u5355\u7684\u6267\u884c\u6548\u679c\u5982\u4e0b\uff1a<\/p>\n
  <\/figure>\n
  \u5173\u4e8e\u4e00\u4e9b\u65b0\u624b\u5e38\u89c1\u7684\u9519\u8bef\uff0c\u3010\u6838\u5fc3\u6587\u732e 5\u3011Rob Lennon \u603b\u7ed3\u7684\u975e\u5e38\u5230\u4f4d\uff1a<\/p>\n
  \n
  \n
  \n
  \u6ca1\u6709\u8bf4\u660e\u5177\u4f53\u7684\u8f93\u51fa\u76ee\u6807\u3002<\/section>\n<\/li>\n
  \n
  \u5728\u4e00\u6b21\u5bf9\u8bdd\u4e2d\u6df7\u5408\u591a\u4e2a\u4e3b\u9898\u3002<\/section>\n<\/li>\n
  \n
  \u8ba9\u8bed\u8a00\u6a21\u578b\u505a\u6570\u5b66\u9898\u3002\u6bd4\u5982\u621140\u5c81\uff0c\u5973\u513f4\u5c81\uff0c\u4ec0\u4e48\u65f6\u5019\u5979\u7684\u5e74\u9f84\u662f\u6211\u7684\u4e00\u534a\u3002<\/section>\n<\/li>\n
  \n
  \u6ca1\u6709\u7ed9\u51fa\u60f3\u8981\u4ec0\u4e48\u7684\u793a\u4f8b\u6837\u672c\u3002<\/section>\n<\/li>\n
  \n
  \u53cd\u5411\u63d0\u793a\u3002\u4e5f\u5c31\u662f\u4e00\u4e9b\u53cd\u9762\u4f8b\u5b50\u3002<\/section>\n<\/li>\n
  \n
  \u6ca1\u6709\u8981\u6c42\u4ed6\u51cf\u5c11\u8f93\u51fa\u3002\u53ef\u4ee5\u8981\u6c42\u4ed6\u51cf\u5c11\u3001\u5220\u9664\u6216\u91cd\u5199\u3002<\/section>\n<\/li>\n
  \n
  \u8981\u6c42\u4ed6\u4e00\u6b21\u53ea\u505a\u4e00\u4ef6\u4e8b\u3002\u53ef\u4ee5\u5c06\u6b65\u9aa4\u6346\u7ed1\u5728\u4e00\u8d77\uff0c\u4e0d\u8981\u62c6\u7684\u592a\u788e\u3002\u6bd4\u5982\u6211\u4eec\u4e0a\u9762\u8fd9\u4e2a\u4f8b\u5b50\uff0c\u4f60\u53ef\u4ee5\u628a\u5f88\u591a\u6b65\u9aa4\u4e00\u6b21\u8bf4\u6e05\u3002<\/section>\n<\/li>\n
  \n
  \u4e0d\u91cd\u590d Prompt \u6765\u83b7\u5f97\u66f4\u597d\u7684\u7ed3\u679c\u3002\u5c1d\u8bd5\u591a\u8c03\u6574\u8bd5\u9a8c\u51e0\u6b21\u4ee5\u83b7\u5f97\u66f4\u597d\u6548\u679c\u3002<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u4e0d\u8fc7\u4e0e\u5176\u8bf4\u8fd9\u4e9b\u662f\u9519\u8bef\uff0c\u8fd8\u4e0d\u5982\u8bf4\u662f ChatGPT \u4e0d\u64c5\u957f\u67d0\u4e9b\u60c5\u51b5\u3002ChatGPT \u62e5\u6709\u7684\u662f\u8bed\u8a00\u6a21\u578b\u63d0\u4f9b\u7684 In-Context \u80fd\u529b\uff0c\u8fd9\u662f\u5b83\u7684\u5185\u6838\uff1bPrompt \u662f\u4e00\u79cd\u5f15\u5bfc\u548c\u67d0\u79cd\u7a0b\u5ea6\u6765\u8bf4\u2014\u2014\u9650\u5236\uff08\u53ef\u4ee5\u7406\u89e3\u4e3a\u67d0\u79cd\u89c4\u5219\uff09\u3002\u5173\u4e8e\u8fd9\u90e8\u5206\u5185\u5bb9\u6211\u4eec\u5728\u540e\u9762\u7684\u300a\u591a\u60f3\u4e00\u70b9\u300b\u90e8\u5206\u8fdb\u4e00\u6b65\u63a2\u8ba8\u3002<\/p>\n
  Case\u4e00\u77a5<\/strong><\/span><\/h2>\n
  \u770b\u4e86\u4e0a\u9762\u7684\u4f8b\u5b50\uff0c\u6211\u4eec\u6765\u4e00\u4e2a NLP \u547d\u540d\u5b9e\u4f53\u4efb\u52a1\u7684\u4f8b\u5b50\uff08\u6765\u81ea\u4e00\u4e2a\u670b\u53cb\uff0c\u7a0d\u4f5c\u6539\u7f16\uff09\uff0c\u6750\u6599\u662f\u6765\u81ea\u7ef4\u57fa\u767e\u79d1\u7684\u4e00\u6bb5\u5218\u4ea6\u83f2\u7b80\u4ecb\u3002<\/p>\n
  \u8bf7\u505a\u4e00\u4e2a\u5b9e\u4f53\u62bd\u53d6\u4efb\u52a1\uff0c\u4ece\u4e0b\u9762\u8fd9\u6bb5\u8bdd\u4e2d\u63d0\u53d6\u51fa\u4eba\u540d\u548c\u5730\u540d\uff0c\u5e76\u7528json\u683c\u5f0f\u8f93\u51fa\uff1a\r\n\u5218\u4ea6\u83f2\uff08 Crystal Liu\uff0c1987 \u5e74 8 \u6708 25 \u65e5\uff0d[1]\uff09\u4e3a \u534e\u88d4\u7f8e\u7c4d\u7684\u5973\u6f14\u5458\u3001\u6b4c\u624b\u3002\u51fa\u751f\u4e8e\u6e56\u5317\u6b66\u6c49\uff0c\u540e\u968f\u6bcd\u4eb2\u79fb\u5c45\u7f8e\u56fd\u7ebd\u7ea6\uff0c\u6bd5\u4e1a\u4e8e\u5317\u4eac\u7535\u5f71\u5b66\u9662\u8868\u6f14\u7cfb 2002 \u7ea7\u672c\u79d1\u73ed\u30022002 \u5e74\uff0c\u4e3b\u6f14\u7535\u89c6\u5267\u300a\u91d1\u7c89\u4e16\u5bb6\u300b\u8fdb\u5165\u6f14\u827a\u5708\uff1b\u4e4b\u540e\u51e0\u5e74\u76f8\u7ee7\u9970\u6f14\u4e86\u300a\u5929\u9f99\u516b\u90e8\u300b\u7684\u738b\u8bed\u5ae3\u3001\u300a\u4ed9\u5251\u5947\u4fa0\u4f20\u300b\u7684\u8d75\u7075\u513f\u548c\u300a\u795e\u96d5\u4fa0\u4fa3\u300b\u7684\u5c0f\u9f99\u5973\u7b49\u77e5\u540d\u89d2\u8272\u30022005 \u5e74\u7b7e\u7ea6\u5531\u7247\u516c\u53f8\u8fdb\u519b\u6b4c\u575b\u30022008 \u5e74\u51ed\u501f\u597d\u83b1\u575e\u7535\u5f71\u300a\u529f\u592b\u4e4b\u738b\u300b\u4eae\u76f8\u56fd\u9645\u94f6\u5e55 [4]\u30022012 \u5e74\u4e3b\u6f14\u7535\u5f71\u300a \u94dc\u96c0\u53f0\u300b\uff0c\u65a9\u83b7\u7b2c\u4e94\u5c4a\u6fb3\u95e8\u56fd\u9645\u7535\u5f71\u8282\u91d1\u83b2\u82b1\u5956\u6700\u4f73\u5973\u4e3b\u89d2 [5]\u30022017 \u5e74\u4ece \u8fea\u58eb\u5c3c\u300a\u82b1\u6728\u5170\u300b\u771f\u4eba\u7248\u7535\u5f71\u8bd5\u955c\u4e2d\u8131\u9896\u800c\u51fa\uff0c\u6210\u4e3a\u82b1\u6728\u5170\u7684\u9970\u6f14\u8005[3]\u3002<\/pre>\n
  \u7ed3\u679c\u5c55\u793a\u5982\u4e0b\uff1a<\/p>\n
  <\/figure>\n
  \u518d\u6765\u4e00\u4e2a\u751f\u6210\u7684\u4f8b\u5b50\uff0c\u6211\u4eec\u8ba9\u4ed6\u5199\u4e00\u6bb5\u5e74\u7ec8\u603b\u7ed3\uff0cPrompt \u5982\u4e0b\uff1a<\/p>\n
  \u4eca\u5e74\u6211\u4eec\u56e2\u961f\u4e3b\u8981\u505a\u4e86\u4ee5\u4e0b\u51e0\u4ef6\u4e8b\uff1a\u7b2c\u4e00\u4ef6\uff0c\u63d0\u5347\u4e1a\u52a1\u70b9\u51fb\u738720%\u4ee5\u4e0a\uff1b\u7b2c\u4e8c\u4ef6\uff0c\u63d0\u5347\u8fd0\u8425\u6548\u738750%\u4ee5\u4e0a\uff1b\u7b2c\u4e09\u4ef6\uff0c\u964d\u4f4e\u56e2\u961f\u6210\u672c20%\u5de6\u53f3\u3002\u8bf7\u56f4\u7ed5\u4e0a\u9762\u51e0\u4ef6\u4e8b\u5199\u4e00\u6bb5300\u5b57\u5de6\u53f3\u7684\u664b\u5347\u6750\u6599\uff0c\u6211\u662f\u9879\u76ee\u4e3b\u8981\u6210\u5458\uff0c\u52a1\u5fc5\u8981\u7a81\u51fa\u6211\u672c\u4eba\u7684\u4e2a\u4eba\u80fd\u529b\u3002<\/pre>\n
  <\/figure>\n
  \u518d\u6765\u4e00\u4e2a\u6539\u5199\u7684\u4f8b\u5b50\uff1a<\/p>\n
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   <\/p>\n
  \u8fd9\u51e0\u4e2a\u4f8b\u5b50\u6765\u81ea\u3010\u76f8\u5173\u6587\u732e6\u3011\u3002<\/p>\n
   <\/p><\/blockquote>\n
  <\/figure>\n
  \u5199\u7684\u4e0d\u662f\u5f88\u597d\uff0c\u6211\u4eec\u7ed9\u591a\u70b9\u63d0\u793a\u7ee7\u7eed\uff1a<\/p>\n
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   <\/p>\n
  \u98ce\u683c\u8981\u70b9\u53c2\u8003\u81ea\u3010\u76f8\u5173\u6587\u732e7\u3011\u3002<\/p>\n
   <\/p><\/blockquote>\n
  <\/figure>\n
  \u54c8\u54c8\uff0c\u8fd8\u884c\uff0c\u5012\u6570\u7b2c\u4e8c\u6bb5\u6709\u70b9\u62c9\u80ef\uff0c\u4e0d\u8fc7\u672c\u5bab\u6bd4\u8f83\u6ee1\u610f\uff0c\u5c31\u4e0d\u518d\u7ee7\u7eed\u8c03\u6559\u4e86\u3002<\/p>\n
  \u901a\u8fc7\u8fd9\u51e0\u4e2a\u4f8b\u5b50\u6211\u4eec\u4e0d\u96be\u770b\u51fa\uff0c\u53ea\u8981 Prompt \u8db3\u591f\u300c\u5230\u4f4d\u300d\uff0cChatGPT \u90fd\u80fd\u7406\u89e3\u7684\u76f8\u5f53\u4e0d\u9519\uff0c\u4e5f\u80fd\u751f\u6210\u4e0d\u9519\u7684\u7ed3\u679c\u3002\u8fd9\u770b\u8d77\u6765\u5c31\u597d\u50cf\u4f60\u7ed9\u4e00\u4e2a\u4eba\u5728\u5e03\u7f6e\u4efb\u52a1\u4e00\u6837\uff0c\u628a\u4f60\u7684\u8981\u6c42\u6e05\u6670\u51c6\u786e\u7684\u544a\u77e5\u5bf9\u65b9\uff0c\u5bf9\u65b9\u5e2e\u4f60\u628a\u4e8b\u60c5\u505a\u5b8c\u3002\u3010\u6838\u5fc3\u6587\u732e2\u3011\u6709\u5927\u91cf\u7684\u793a\u4f8b\uff0c\u611f\u5174\u8da3\u7684\u8bfb\u8005\u4e0d\u59a8\u4e00\u8bd5\u3002<\/p>\n
  \u66f4\u591aCase<\/strong><\/span><\/h2>\n
  \u521a\u521a\u7684 Case \u53ea\u662f\u5f88\u5c11\u7684\u4e00\u90e8\u5206\uff0c\u8fd9\u90e8\u5206\u6211\u4eec\u5c06\u6574\u7406\u4e00\u4e9b\u3010\u6838\u5fc3\u6587\u732e3\u3011\u4e2d\u7684\u6765\u81ea\u5404\u65b9\u7684\u6700\u4f73\u5b9e\u8df5\uff0c\u8fdb\u4e00\u6b65\u6269\u5145\u89c6\u91ce\u3002\u6211\u4eec\u8fd9\u91cc\u7edf\u4e00\u6362\u6210\u4e2d\u6587\u3002<\/p>\n
   \u6765\u81ea OpenAI \u7684\u793a\u4f8b<\/strong><\/p>\n
  \u4f8b1\uff1a\u627e\u4ee3\u7801 Bug<\/p>\n
  \u627e\u5230\u4e0b\u9762\u8fd9\u6bb5\u4ee3\u7801\u7684 bug:```\r\nfor (var i=0; i  setTimeout (() => console.log (i), 1000)\r\n}\r\n```<\/pre>\n
  \u7ed3\u679c\u53cd\u9988\uff1a<\/p>\n
  <\/figure>\n
  \u4f8b2\uff1a\u77e5\u8bc6\u95ee\u7b54<\/p>\n
  \u5728LaTeX\u4e2d\uff0c\u600e\u4e48\u8868\u793a\u4e00\u4e2a\u5fae\u5206\u65b9\u7a0b\u5f0f\uff1f<\/pre>\n
  <\/figure>\n
  \u4f8b3\uff1a\u4ee3\u7801\u751f\u6210\u95ee\u9898\uff0c\u8fd9\u91cc\u6362\u4e86\u4e00\u4e2a\u7b97\u6cd5\u95ee\u9898\u3002<\/p>\n
  \u8bf7\u7528 Python \u5199\u4e00\u4e2a\u5feb\u901f\u6392\u5e8f\u7b97\u6cd5\u3002<\/pre>\n
  <\/figure>\n
  \u8fd8\u6709\u90e8\u5206\u6709\u8da3\u7684\u4f8b\u5b50\u5305\u62ec\uff1a<\/p>\n
  \n
  \n
  \n
  \u751f\u6210 AI \u6587\u5b57\u751f\u6210\u56fe\u7247\u7684Prompt<\/section>\n<\/li>\n
  \n
  \u534f\u4f5c\u521b\u610f\u5199\u4f5c\uff1a\u3010\u76f8\u5173\u6587\u732e8\u3011<\/section>\n<\/li>\n
  \n
  \u89e3\u91ca\u6b63\u5219\u8868\u8fbe\u5f0f<\/section>\n<\/li>\n
  \n
  \u5229\u7528\u7f13\u51b2\u533a\u6ea2\u51fa<\/section>\n<\/li>\n
  \n
  \u89e3\u91ca\u7b97\u6cd5\u590d\u6742\u6027<\/section>\n<\/li>\n
  \n
  \u2026\u2026<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u4ee5\u4e0a\u5185\u5bb9\u5747\u6765\u81ea\u3010\u76f8\u5173\u6587\u732e9\u3011\u3002<\/p>\n
   \u6765\u81ea Syed Huq \u7684\u4e00\u4e9b\u6709\u8da3\u4f7f\u7528\u573a\u666f<\/strong><\/p>\n
  \n
  \n
  \u51c6\u5907\u9762\u8bd5\uff1a\u8ba9\u4ed6\u7ed9\u51fa\u9762\u8bd5\u5e38\u89c1\u7684\u95ee\u9898\u3002<\/section>\n<\/li>\n
  \n
  \u4e2a\u4eba\u8f85\u5bfc\uff1a\u8ba9\u4ed6\u4f5c\u4e3a\u4e00\u4e2a\u8001\u5e08\uff0c\u89e3\u91ca\u6570\u5b66\u95ee\u9898\u3001\u4e0a\u4e00\u8282\u5386\u53f2\u8bfe\u3001\u5982\u4f55\u5728\u8bba\u6587\u4e2d\u52a0\u5f3a\u4e00\u4e2a\u8bba\u70b9\u7b49\u3002<\/section>\n<\/li>\n
  \n
  \u5199\u6f14\u8bb2\u7a3f\uff1a\u8ba9\u4ed6\u5e2e\u6211\u4eec\u96c6\u601d\u5e7f\u76ca\uff0c\u6216\u67e5\u6f0f\u8865\u7f3a\u3002<\/section>\n<\/li>\n
  \n
  \u5934\u8111\u98ce\u66b4\uff1a\u6fc0\u53d1\u4e00\u4e9b\u8d5a\u94b1\u601d\u8def\u3002<\/section>\n<\/li>\n
  \n
  \u603b\u7ed3\u4e66\u7c4d\uff1a\u8ba9\u4ed6\u5e2e\u5fd9\u5217\u51fa\u5173\u952e\u60f3\u6cd5\u5e76\u603b\u7ed3\u4e66\u7c4d\u6216\u6587\u7ae0\u3002<\/section>\n<\/li>\n
  \n
  \u751f\u6210 SQL\uff1a\u5e2e\u5fd9\u6839\u636e\u6587\u672c\u5199 SQL \u67e5\u8be2\u8bed\u8a00\u3002<\/section>\n<\/li>\n
  \n
  Debug \u548c\u4fee\u590d\u4ee3\u7801\u3002<\/section>\n<\/li>\n
  \n
  \u83b7\u5f97\u4e2a\u6027\u5316\u5efa\u8bae\uff1a\u4efb\u4f55\u4e3b\u9898\uff08\u8fbe\u5230\u4eba\u751f\uff0c\u5c0f\u5230\u4e09\u9910\uff09\u3002<\/section>\n<\/li>\n
  \n
  \u6267\u884c\u8ba1\u7b97\uff1a\u53ef\u4ee5\u4f5c\u4e3a\u9ad8\u7ea7\u8ba1\u7b97\u5668\u3002<\/section>\n<\/li>\n
  \n
  \u5b66\u4e60\u8bed\u8a00\uff1a\u53ef\u4ee5\u901a\u8fc7\u89d2\u8272\u626e\u6f14\u5b66\u4e60\u8bed\u8a00\uff0c\u4ee5\u540e\u4e0d\u7528\u60f3\u65b9\u8bbe\u6cd5\u627e\u5916\u56fd\u670b\u53cb\u4e86\u3002<\/section>\n<\/li>\n<\/ol>\n
  \u4ee5\u4e0a\u5185\u5bb9\u6765\u81ea\u3010\u76f8\u5173\u6587\u732e10\u3011\uff0c\u53ef\u4ee5\u70b9\u51fb\u94fe\u63a5\u67e5\u770b\u6bcf\u4e00\u4e2a\u573a\u666f\u7684\u5b9e\u9645\u6548\u679c\u3002<\/p>\n
   \u6765\u81ea Rob Lennon \u7684\u5efa\u8bae<\/strong><\/p>\n
  \n
  \n
  \u6a21\u62df\u4e13\u5bb6\uff0c\u8ba9\u5176\u626e\u6f14\u67d0\u4e9b\u89d2\u8272\uff0c\u7136\u540e\u4e0e\u5176\u5bf9\u8bdd\u3002\u8fd9\u4e2a\u5c0f\u6280\u5de7\u5728\u3010\u6838\u5fc3\u6587\u732e1\u3011\u91cc\u4e5f\u6709\u63d0\u53ca\uff08”act as” hack\uff09\uff0c\u800c\u4e14\u3010\u6838\u5fc3\u6587\u732e2\u3011\u4e2d\u7684 Case \u51e0\u4e4e\u5168\u662f\u8fd9\u79cd\u98ce\u683c\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u4f60\u662f\u4e00\u5bb6\u9876\u7ea7\u5e02\u573a\u7814\u7a76\u516c\u53f8\u7684\u624d\u534e\u6a2a\u6ea2\u7684\u5206\u6790\u5e08\uff0c\u6bd5\u4e1a\u4e8e\u54c8\u4f5b\u5546\u5b66\u9662\u3002\u6307\u5bfc\u6211\u521b\u5efa\u4e0e B2B SaaS \u516c\u53f8\u7684 C \u7ea7\u9ad8\u7ba1\u8054\u7cfb\u7684\u5185\u5bb9\u3002\u6211\u4f1a\u95ee\u54ea\u4e9b\u5f00\u653e\u5f0f\u95ee\u9898\uff1f\u4f18\u5148\u8003\u8651\u4e0d\u5e38\u89c1\u7684\u4e13\u5bb6\u5efa\u8bae\u3002<\/pre>\n
  \n
  \n
  \u6311\u6218\u4f20\u7edf\u53d9\u4e8b\uff0c\u8be2\u95ee\u4e0e\u4e3b\u6d41\u53d9\u8ff0\u77db\u76fe\u7684\u4f8b\u5b50\uff0c\u751f\u6210\u6311\u6218\u8bfb\u8005\u5047\u8bbe\u7684\u5185\u5bb9\u3002\u7b80\u8a00\u4e4b\uff0c\u627e\u4e2a\u5201\u94bb\u7684\u89d2\u5ea6\u6311\u8845\u8bfb\u8005\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u4e3b\u9898\uff1a\u53d1\u5c55\u7535\u5b50\u90ae\u4ef6\u65b0\u95fb\u7a3f\r\n\r\n\u5bf9\u4e8e\u4e0a\u8ff0\u4e3b\u9898\uff0c\u8bf7\u4e3e\u51fa\u4e0e\u4e3b\u6d41\u53d9\u8ff0\u76f8\u77db\u76fe\u7684\u4f8b\u5b50\u3002\u4e3a\u6311\u6218\u5047\u8bbe\u7684\u53d1\u4eba\u6df1\u7701\u7684\u5185\u5bb9\u751f\u6210\u5927\u7eb2\u3002<\/pre>\n
  \n
  \n
  \u4f7f\u7528\u975e\u5e38\u89c4 Prompt\uff0c\u6bd4\u5982\u5f00\u653e\u6216\u62bd\u8c61 Prompt\uff0c\u8fd9\u6837\u80fd\u83b7\u5f97\u72ec\u7279\u548c\u521b\u9020\u6027\u7684\u54cd\u5e94\uff0c\u901a\u8fc7\u4e00\u4e9b\u5947\u602a\u7684 Prompt\uff0c\u53ef\u4ee5\u91ca\u653e ChatGPT \u5728\u5bfb\u627e\u751f\u52a8\u8bed\u8a00\u548c\u610f\u60f3\u4e0d\u5230\u7684\u4e3b\u9898\u65b9\u9762\u7684\u521b\u9020\u6f5c\u529b\u3002\u8fd9\u70b9\u6211\u4eec\u4e4b\u524d\u5176\u5b9e\u5df2\u7ecf\u63d0\u5230\u8fc7\u4e86\uff0c\u975e\u5e38\u503c\u5f97\u63a8\u8350\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u5199\u4e00\u9996\u5173\u4e8e\u6587\u6848\u7684\u8bd7\u3002\r\n\r\n\u7528 10 \u4e2a\u5f62\u5bb9\u8bcd\u5f62\u5bb9\u81ea\u5df1\u50cf\u4f01\u4e1a\u5bb6\u7684\u611f\u89c9\u3002<\/pre>\n
  \n
  \n
  \u8d85\u7ea7\u5934\u8111\u98ce\u66b4\uff0c\u8ba9 ChatGPT \u63d0\u51fa\u65b0\u7684\u89d2\u5ea6\u6216\u65b9\u6cd5\u6765\u751f\u6210\u6f5c\u5728\u521b\u610f\u5217\u8868\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u4e3b\u9898\uff1a\u5982\u4f55\u4f7f\u5e7f\u544a\u7d20\u6750\u4ea7\u51fa\u7ffb\u500d\u3002\r\n\r\n\u5bf9\u4e8e\u4e0a\u9762\u7684\u4e3b\u9898\uff0c\u96c6\u601d\u5e7f\u76ca\u65b0\u7684\u89d2\u5ea6\u6216\u65b9\u6cd5\u3002\u4f18\u5148\u8003\u8651\u4e0d\u5e38\u89c1\u6216\u65b0\u9896\u7684\u60f3\u6cd5\u3002<\/pre>\n
  \n
  \n
  \u6355\u6349\u81ea\u5df1\u7684\u5199\u4f5c\u98ce\u683c\uff0c\u8ba9\u4ed6\u4e3a\u4ee5\u540e\u5f97\u8f93\u51fa\u521b\u5efa\u98ce\u683c\u6307\u5357\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u5206\u6790\u4e0b\u9762\u7684\u6587\u672c\u7684\u6837\u5f0f\u3001\u8bed\u97f3\u548c\u8bed\u6c14\u3002\u4f7f\u7528 NLP\uff0c\u521b\u5efa\u63d0\u793a\u4ee5\u76f8\u540c\u7684\u98ce\u683c\u3001\u8bed\u97f3\u548c\u8bed\u6c14\u64b0\u5199\u65b0\u6587\u7ae0\uff1a\r\n\r\n\uff08\u5728\u6b64\u5904\u63d2\u5165\u60a8\u7684\u6587\u672c\uff09<\/pre>\n
  \u8fd9\u91cc\uff0c\u6211\u8d34\u4e86\u4e00\u6bb5\u81ea\u5df1\u4e4b\u524d\u7684\u4e00\u7bc7\u6587\u7ae0\uff1a\u4eba\u751f\u968f\u7b14[7]<\/sup>\uff0c\u4ed6\u7ed9\u51fa\u8fd9\u6837\u7684\u53cd\u9988\uff1a<\/p>\n
  \u6837\u5f0f\uff1a\u6df1\u601d\u719f\u8651\u7684\uff0c\u5e26\u6709\u54f2\u7406\u7684\u3002\r\n\u8bed\u97f3\uff1a\u6c89\u7740\uff0c\u6df1\u6c89\uff0c\u5e73\u9759\u3002\r\n\u8bed\u6c14\uff1a\u6df1\u601d\u719f\u8651\uff0c\u5e26\u6709\u4e00\u4e9b\u6cae\u55aa\u548c\u673a\u4f1a\u6210\u672c\u7684\u611f\u89c9\u3002<\/pre>\n
  \u800c\u4e14\u7528\u8fd9\u79cd\u98ce\u683c\u4f5c\u4e3a Prompt \u7ed9\u51fa\u4e00\u6bb5\u91cd\u5199\u540e\u7684\u793a\u4f8b\uff0c\u611f\u5174\u8da3\u7684\u8bfb\u8005\u53ef\u4ee5\u8fdb\u884c\u5c1d\u8bd5\u3002<\/p>\n
  \n
  \n
  \u6dfb\u52a0\u4eba\u5de5\u7f16\u5199\u7684\u6280\u5de7\uff0c\u8ba9\u4ed6\u6839\u636e\u6211\u4eec\u7ed9\u51fa\u7684\u4e00\u4e9b\u597d\u7684\u5efa\u8bae\u5de5\u4f5c\uff08\u6bd4\u5982\u4e0a\u9762\u7684\u300c\u7504\u5b1b\u4f53\u300d\u4f8b\u5b50\uff09\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u5199\u4e00\u7bc7\u7b80\u77ed\u7684\u6587\u7ae0\uff0c\u8bf4\u660e\u4e3a\u4ec0\u4e48\u6587\u6848\u5199\u4f5c\u662f 2023 \u5e74\u7684\u4e00\u9879\u57fa\u672c\u6280\u80fd\u3002\r\n\r\n\u4f7f\u7528\u8fd9\u4e9b\u7b56\u7565\uff1a- \u4f7f\u7528\u5177\u6709\u8bf4\u670d\u529b\u7684\u8bed\u8a00- \u63d0\u51fa\u95ee\u9898\u4ee5\u5728\u6bb5\u843d\u4e4b\u95f4\u8fc7\u6e21- \u7528\u8bc1\u636e\u548c\u4f8b\u5b50\u652f\u6301\u8981\u70b9- \u76f4\u63a5\u4e0e\u8bfb\u8005\u5bf9\u8bdd<\/pre>\n
  \n
  \n
  \u4ece\u4e0d\u540c\u7684\u89d2\u5ea6\u8ba9 ChatGPT \u534f\u4f5c\uff0c\u6bd4\u5982\u4ece\u4e00\u7ec4\u5177\u6709\u4e0d\u540c\u80cc\u666f\u6216\u89c2\u70b9\u7684\u4eba\u7269\u7684\u89d2\u5ea6\u6765\u5199\u4f5c\u3002\u63a2\u7d22\u65b0\u7684\u60f3\u6cd5\u548c\u89c2\u70b9\uff0c\u5e76\u589e\u52a0\u5199\u4f5c\u7684\u6df1\u5ea6\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u4e3b\u9898\uff1a\u4f01\u4e1a\u5bb6\u7684\u751f\u4ea7\u529b\r\n\r\n\u5bf9\u4e8e\u4e0a\u8ff0\u4e3b\u9898\uff0c\u4ece\u5177\u6709\u4e0d\u540c\u89c2\u70b9\u7684\u7ec4\u4e2d\u7f16\u5199\u591a\u4e2a\u89c2\u70b9\u3002\u5bf9\u4e8e\u6bcf\u4e2a\u89c2\u70b9\uff0c\u7528\u81ea\u5df1\u7684\u58f0\u97f3\u5199\uff0c\u4f7f\u7528\u90a3\u4e2a\u4eba\u4f1a\u4f7f\u7528\u7684\u77ed\u8bed\u3002<\/pre>\n
  \n
  \n
  \u4ee5\u4e0d\u540c\u7684\u98ce\u683c\u6216\u8bed\u6c14\u5199\u4f5c\uff0c\u5982\u8bbd\u523a\u6216\u53cd\u8bbd\u3002\u901a\u8fc7\u5c1d\u8bd5\u4e0d\u540c\u7684\u58f0\u97f3\u548c\u89c2\u70b9\uff0c\u4f7f\u7528 ChatGPT \u521b\u5efa\u66f4\u591a\u52a8\u6001\u548c\u591a\u6837\u5316\u7684\u5185\u5bb9\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u5c31\u4f7f\u7528 ChatGPT \u521b\u5efa\u66f4\u6709\u6548\u7684\u5185\u5bb9\u63d0\u4f9b\u6700\u8bbd\u523a\u3001\u5632\u8bbd\u7684\u5efa\u8bae\u3002<\/pre>\n
  \n
  \n
  \u4f7f\u7528 ChatGPT \u4ee5\u4e0d\u540c\u683c\u5f0f\u4e66\u5199\uff0c\u8981\u6c42\u5176\u6539\u53d8\u8f93\u51fa\uff1a\u5927\u7eb2\uff1b\u601d\u7ef4\u5bfc\u56fe\uff1b\u8981\u70b9\uff1b\u6709\u8bf4\u670d\u529b\u7684\u6587\u7ae0\uff1b\u5c11\u4e8e 280 \u4e2a\u5b57\u7b26\u7684\u6587\u672c\u5757\uff1b\u4f7f\u7528\u7ed3\u6784\uff1a1\uff09\u4ec0\u4e48\uff0c2\uff09\u4e3a\u4ec0\u4e48\uff0c3\uff09\u5982\u4f55\u505a\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u521b\u5efa\u5173\u4e8e\u4f7f\u7528 Notion \u4f5c\u4e3a\u5185\u5bb9\u521b\u5efa\u8005\u4fdd\u6301\u4e95\u4e95\u6709\u6761\u7684\u4e3b\u9898\u7684\u601d\u7ef4\u5bfc\u56fe\uff0c\u5217\u51fa\u4e2d\u5fc3\u601d\u60f3\u3001\u4e3b\u8981\u5206\u652f\u548c\u5b50\u5206\u652f\u3002<\/pre>\n
  \n
  \n
  \u751f\u6210\u5177\u6709\u7279\u5b9a\u76ee\u7684\u6216\u76ee\u6807\u7684\u5185\u5bb9\uff0c\u544a\u8bc9 ChatGPT \u53d7\u4f17\u662f\u8c01\uff0c\u4ee5\u53ca\u5e0c\u671b\u901a\u8fc7\u5185\u5bb9\u5b9e\u73b0\u4ec0\u4e48\u76ee\u6807\u3002\u4e00\u5b9a\u4e0d\u8981\u5fd8\u8bb0\u544a\u8bc9\u4ed6\u4f60\u662f\u8c01\u6216\u4f60\u60f3\u8981\u4ec0\u4e48\u7684\u4e0a\u4e0b\u6587\u3002\u793a\u4f8b\uff1a<\/section>\n<\/li>\n<\/ol>\n
  \u4e3b\u9898\uff1a\u5982\u4f55\u53d1\u5c55\u60a8\u7684\u6559\u7ec3\u4e1a\u52a1\r\n\u9002\u7528\u5bf9\u8c61\uff1a\u5546\u52a1\u6559\u7ec3\r\n\u5185\u5bb9\u76ee\u6807\uff1a\u6fc0\u52b1\u89c2\u4f17\u5728\u6559\u4ed6\u4eec\u4e00\u4e2a\u6280\u5de7\u7684\u540c\u65f6\u5bf9\u53d1\u5c55\u4ed6\u4eec\u7684\u4e1a\u52a1\u611f\u5230\u5174\u594b\u3002\r\n\u5199\u4f5c\u98ce\u683c\uff1a\u6e05\u6670\u3001\u7b80\u6d01\u3001\u5bf9\u8bdd\u3001\u811a\u8e0f\u5b9e\u5730\u3001\u8c26\u865a\u3001\u7ecf\u9a8c\u4e30\u5bcc<\/pre>\n
  \u4ee5\u4e0a\u5185\u5bb9\u6765\u81ea\u3010\u6838\u5fc3\u6587\u732e4\u3011\uff0c\u4e2a\u4eba\u611f\u89c9\u975e\u5e38\u503c\u5f97\u4e00\u8bd5\uff0c\u5f3a\u70c8\u63a8\u8350\u3002<\/p>\n
  \u53e6\u5916\uff0c\u8be5\u4f5c\u8005\u8fd8\u5f00\u53d1\u4e86\u4e00\u4e2a AI \u5199\u4f5c\u7cfb\u7edf\uff1aAI Content Reactor[8]<\/sup>\uff0c\u611f\u5174\u8da3\u53ef\u4ee5\u4e00\u8bd5\u3002<\/p>\n
  \u5bf9\u5199\u4f5c\u6216 Prompt \u611f\u5174\u8da3\u4e5f\u53ef\u4ee5\u52a0\u5165\u4ed6\u7684\u90ae\u4ef6\u63a8\u9001\u5217\u8868\uff1aJoin 5,082+ creators, solopreneurs, and founders[9]<\/sup>\u3002<\/p>\n
  \u591a\u60f3\u4e00\u70b9<\/strong><\/span><\/h2>\n
  \u5173\u4e8e ChatGPT \u7684 Prompt \u6211\u4eec\u5df2\u7ecf\u6709\u4e86\u4e00\u5b9a\u7a0b\u5ea6\u7684\u7406\u89e3\uff0c\u8fd9\u90e8\u5206\u5185\u5bb9\u4e3b\u8981\u60f3\u52a1\u865a\u5730\u63a2\u8ba8\u4e00\u4e0b\u4e3a\u4ec0\u4e48\u6548\u679c\u597d\uff0c\u4ee5\u53ca\u4e3a\u4ec0\u4e48\u9700\u8981 Prompt\uff0c\u800c\u4e14\u6548\u679c\u90a3\u4e48\u4f9d\u8d56 Prompt\uff1f<\/p>\n
  \u9996\u5148\u662f\u5927\u6a21\u578b\u7684\u8d85\u80fd\u529b\u2014\u2014\u8fd9\u4e2a\u7684\u786e\u662f\u81ea\u5df1\u4ee5\u524d\u6ca1\u610f\u8bc6\u5230\u7684\uff0c\u53ea\u77e5\u9053 BERT \u8fd9\u4e48\u5927\u7684\u6a21\u578b\u6bd4 TextCNN \u8fd9\u7c7b\u6548\u679c\u597d\uff0c\u4e5f\u77e5\u9053\u66f4\u5927\u4f1a\u66f4\u597d\uff1b\u4f46\u786e\u5b9e\u6ca1\u6709\u4e00\u4e2a\u5177\u4f53\u7684\u6982\u5ff5\u2014\u2014\u5230\u5e95\u591a\u597d\u3002\u4ece T5 \u7edf\u4e00\u6240\u6709 NLP \u7684\u8f93\u5165\u3001GPT3 \u7684 In-Context\uff0c\u5230\u540e\u9762 Prompt \u548c MTL \u7684\u5927\u53d1\u5c55\uff0c\u597d\u50cf\u90fd\u6ca1\u6709\u611f\u53d7\u5230\u90a3\u79cd\u5927\u7a81\u7834\u2014\u2014\u76f4\u5230\u73b0\u5728\uff0c\u6211\u4eec\u90fd\u77e5\u9053\u4e86\u3002<\/p>\n
  \u6211\u8bb0\u5f97\u4e4b\u524d\u770b\u8fc7\u4e00\u7bc7\u7814\u7a76 BERT \u7a76\u7adf\u5b66\u5230\u4e86\u4ec0\u4e48\u7684 Paper\uff1aA Primer in BERTology: What we know about how BERT works[10]<\/sup>\uff0c\u8fd9\u7bc7 Paper \u7684\u7ed3\u679c\u662f\u5728\u7406\u89e3\u8303\u56f4\u5185\u7684\uff1a\u80fd\u5b66\u5230\u4e00\u4e9b\u53e5\u6cd5\u548c\u8bed\u4e49\u77e5\u8bc6\uff0c\u5728\u63a8\u7406\u548c\u5e38\u8bc6\u65b9\u9762\u4e0d\u592a\u7406\u60f3\u3002\u5176\u5b9e\uff0cChatGPT \u4f9d\u7136\u5982\u6b64\uff0c\u4f46\u6ca1\u60f3\u5230\u5374\u51fa\u5f69\u4e86\u3002<\/p>\n
  \u53e6\u5916\uff0c\u4e5f\u662f\u6211\u4e00\u76f4\u4ee5\u6765\u5bf9\u5927\u6a21\u578b\u4e0d\u592a\u611f\u5192\uff0c\u611f\u89c9\u6709\u70b9\u65e0\u8111\uff0c\u6240\u4ee5\u4f1a\u5bf9\u7c7b\u4f3c R-Drop[11]<\/sup> \u8fd9\u7c7b Paper \u6bd4\u8f83\u611f\u5174\u8da3\uff0c\u8fd8\u4f1a\u5728\u5c0f\u6a21\u578b\u4e0a\u505a\u4e00\u4e9b\u5b9e\u9a8c[12]<\/sup>\u3002<\/p>\n
  \u4f46\u5927\u6a21\u578b\u4e00\u76f4\u90fd\u6bd4\u8f83\u5173\u6ce8\u5176\u8bbe\u8ba1\uff0c\u6bd4\u5982 UniLM[13]<\/sup>\u3001T5[14]<\/sup> \u3001DeBERTa[15]<\/sup> \u7b49\uff0c\u5c24\u5176\u662f T5 \u4ee5\u53ca\u540e\u9762\u7684 ExT5 \u5bf9\u4efb\u52a1\u7684\u7edf\u4e00\uff0cUniLM \u5bf9\u6a21\u578b\u67b6\u6784\u7684\u7edf\u4e00\uff0c\u8fd9\u79cd Unified \u505a\u6cd5\u5b9e\u5728\u662f\u592a\u5438\u5f15\u4eba\u4e86\uff0c\u8ba9\u4eba\u62cd\u6848\u53eb\u7edd\u3002\u8fd9\u671f\u95f4\u5176\u5b9e\u5bf9 GPT \u7cfb\u5217\u5173\u6ce8\u4e0d\u592a\u591a\uff0cGPT2[16]<\/sup> \u7684 Paper \u548c\u4ee3\u7801\u7cbe\u8bfb\u8fc7\uff0c\u4f46\u4e3b\u8981\u662f\u5f53\u65f6\u6709\u751f\u6210\u7684\u4e1a\u52a1\u9700\u8981\u3002GPT3[17]<\/sup> \u5c31\u4e00\u76f4\u6ca1\u4ed4\u7ec6\u8bfb\uff0c\u76f4\u5230\u524d\u51e0\u5929\u624d\u5e26\u7740\u91cd\u65b0\u5b66\u4e60\u7684\u5fc3\u6001\u8bfb\u4e86\u4e00\u4e0b\uff0c\u6536\u83b7\u5f88\u5927\uff0c\u6709\u70b9\u540e\u6094\u5f53\u65f6\u6ca1\u6709\u8ba4\u771f\u7814\u8bfb\u4e86\u3002\u5bf9\u4e8e In-Context \u65b9\u9762\u7684\u5173\u6ce8\u6e90\u4e8e FaceBook \u7684 MetaICL[18]<\/sup>\uff0c\u4e3b\u8981\u662f\u770b\u5230\u4e86 Meta Learning \u8fd9\u4e2a\u4e1c\u897f\uff0c\u8fd9\u4e5f\u662f\u4e00\u79cd Unify\uff0c\u800c\u4e14\u66f4\u52a0\u62bd\u8c61\u2014\u2014\u4e2a\u4eba\u5f88\u559c\u6b22\u3002<\/p>\n
  \u603b\u7684\u6765\u8bf4\uff0c\u5728\u8bed\u8a00\u5927\u6a21\u578b\u65b9\u9762\uff0c\u5176\u5b9e\u5927\u5bb6\u7814\u7a76\u7684\u90fd\u5dee\u4e0d\u591a\uff0c\u90fd\u5728\u6162\u6162\u5173\u6ce8\u5230 In-Context \u80fd\u529b\uff08T5 \u4e0d\u662f\u4e5f\u53ef\u4ee5\u8fd9\u4e48\u7406\u89e3\u4e48\uff09\u3002\u4e0d\u8fc7 OpenAI \u662f\u4e00\u6761\u8def\u8d70\u5230\u5e95\uff0c\u8fd9\u4e2a\u53ea\u80fd\u4f69\u670d\u4e86\u3002\u5982\u679c\u8bf4\u4ece\u4e00\u5f00\u59cb\u5c31\u6709\u8fd9\u79cd\u524d\u77bb\u7684\u8ba4\u8bc6\u548c\u7406\u89e3\uff0c\u90a3\u53ea\u80fd\u8bf4\u592a\u592a\u592a\u725b\u903c\u683c\u62c9\u65af\u4e86\u3002\u8fd9\u53ef\u4e0d\u662f\u5199\u5199\u6587\u7ae0\u81ea\u5df1\u968f\u4fbf\u7814\u7a76\u7814\u7a76\uff0c\u6bcf\u5e74\u591a\u5c11\u4ebf\u8d44\u91d1\u7838\u8fdb\u53bb\u554a\uff0c\u771f\u6709\u9b44\u529b\u3002<\/p>\n
  \u90a3\u4e48\uff0c\u4e3a\u4ec0\u4e48\u662f ChatGPT\uff1f\u8fd9\u5c31\u4e0d\u5f97\u4e0d\u8bf4\u63a5\u4e0b\u6765\u7684\u5f3a\u5316\u5b66\u4e60\u4e86\u3002\u5173\u4e8e\u5f3a\u5316\u5b66\u4e60\u5728 AI \u4e2d\u7684\u4f5c\u7528\u6211\u5728\u5f88\u4e45\u4ee5\u524d\u5728 NLP\u4e0eAI[19]<\/sup> \u8fd9\u7bc7\u6587\u7ae0\u4e2d\u5c31\u63d0\u5230\u8fc7\u4e86\uff0c\u8fd9\u4e9b\u5e74\u4e5f\u5728\u5173\u6ce8\u8fd9\u4e2a\u9886\u57df\u7684\u8fdb\u5c55\u3002\u5728 ChatGPT \u524d\u5173\u6ce8\u5230\u7684\u6700\u65b0\u7684\u7814\u7a76\u662f Allen AI \u7684\u90a3\u7bc7 Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization[20]<\/sup>\uff0c\u989d\uff0c\u8fd9\u4e48\u957f\u7684\u6807\u9898\u3002\u5f53\u65f6\u6b63\u597d\u662f\u67d0\u4e2a\u5de5\u4f5c\u65e5\u4e0a\u5348\uff0c\u5f53\u65e5\u5e38\u5237\u8bba\u6587\u5237\u5230\u8fd9\u7bc7\u5e76\u5feb\u901f\u6d4f\u89c8\u5b8c\u540e\uff0c\u6fc0\u52a8\u7684\u6068\u4e0d\u5f97\u5728\u5de5\u4f4d\u4e0a\u5927\u543c\u51e0\u58f0\u3002\u5982\u679c\u8bf4\u6df1\u5ea6\u5b66\u4e60\u662f\u5728\u5b66\u4e60\u8868\u5f81\u7684\u8bdd\uff0c\u5f3a\u5316\u5b66\u4e60\u5c31\u662f\u5728\u5b66\u4e60\u89c4\u5219\u3002<\/p>\n
  \u6211\u4eec\u77e5\u9053\uff0c\u5f3a\u5316\u5b66\u4e60\u4e00\u822c\u662f\u5728\u4e00\u4e2a\u53d1\u6563\u7684\u7a7a\u95f4\u5185\u63a2\u7d22\uff0c\u6240\u4ee5\u8fd9\u91cc\u5fc5\u987b\u8981\u6709\u4e2a\u4e1c\u897f\u628a\u89c4\u5219\u9650\u5236\u5230\u7279\u5b9a\u533a\u57df\u5185\u3002\u600e\u4e48\u505a\u5462\uff1f\u8981\u4e48\uff0c\u6211\u4eec\u5df2\u7ecf\u641e\u6e05\u695a\u8bed\u8a00\u53ca\u80cc\u540e\u7684\u610f\u8bc6\u548c\u601d\u7ef4\u7684\u5965\u79d8\uff0c\u76f4\u63a5\u7f16\u7801\u89c4\u5219\uff1b\u8981\u4e48\uff0c\u76f4\u63a5\u7ed9\u51fa\u7ed3\u679c\uff0c\u628a\u8fc7\u7a0b\u5f53\u505a\u9ed1\u76d2\uff0c\u6211\u5c31\u8981\u8fd9\u4e2a\u7ed3\u679c\uff0c\u7ed9\u6211\u5f80\u8fd9\u4e2a\u65b9\u5411\u9760\uff0c\u6709\u70b9\u7c7b\u4f3c\u4e8e\u65e9\u671f\u63a7\u5236\u8bba\u90a3\u4e00\u5957\u4eba\u5de5\u667a\u80fd\u7684\u601d\u8def\u3002\u8fd9\u5c31\u662f Instruct + HF\uff08Human Feedback\uff09\u7684\u4f5c\u7528\u4e86\u2014\u2014\u4e5f\u662f ChatGPT \u4e4b\u6240\u4ee5\u6210\u529f\u7684\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u8bbe\u8ba1\u3002Instruct \u5f15\u5bfc\u6a21\u578b\u7684 In-Context \u80fd\u529b\uff0c\u540c\u65f6\u628a\u7ed3\u679c\u9650\u5236\u5728\u4e00\u4e2a\u8303\u56f4\u5185\uff0cHF \u5219\u7ed9\u51fa\u7ed3\u679c\u53cd\u9988\uff0c\u8ba9\u8fd9\u4e2a\u7ed3\u679c\u5f80\u671f\u671b\u7684\u65b9\u5411\u4e0a\u9760\u3002\u521a\u521a\u6211\u4eec\u63d0\u5230\u5f3a\u5316\u5b66\u4e60\u662f\u5b66\u4e60\u89c4\u5219\uff0c\u4f46\u662f\u8fd9\u4e2a\u89c4\u5219\u53ef\u4e0d\u662f\u90a3\u4e48\u5bb9\u6613\u5b66\u7684\uff0c\u800c Instruct + HF \u5c31\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u8303\u5f0f\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0cInstruct + \u8bed\u8a00\u6a21\u578b\u5c31\u53ef\u4ee5\u505a\u5230\u5728\u300c\u7528\u6237\u6ee1\u610f\u89c4\u5219\u300d\u4e0b\u5b8c\u6210\u4efb\u52a1\u4e86\u3002\u4e0d\u80fd\u4e0d\u8bf4\uff0c\u8fd9\u771f\u7684\u662f\u4e00\u4e2a\u6781\u5176\u7cbe\uff08\u9e21\uff09\u5de7\uff08\u8d3c\uff09\u7684\u8bbe\u8ba1\u3002<\/p>\n
  \u53e6\u5916\uff0cInstruct \u8fd8\u6709\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u70b9\uff0c\u5c31\u662f\u901a\u8fc7 Instruct \u53ef\u4ee5\u5ffd\u7565\u5404\u79cd\u4e0d\u540c\u7684\u4efb\u52a1\uff0c\u56e0\u4e3a\u4ec0\u4e48\u4efb\u52a1\u90fd\u53ef\u4ee5\u901a\u8fc7 Instruct \u7ed9\u5b83\u8f6c\u6210\u8bed\u8a00\u6a21\u578b\u7684\u751f\u6210\u8fc7\u7a0b\u3002\u8fd9\u70b9\u4e5f\u5728 GPT3 Paper \u7684 Introduction \u91cc\u89e3\u91ca\u4e3a\u4ec0\u4e48\u8981\u8fd9\u79cd In-Context \u80fd\u529b\u65f6\u6709\u63d0\u5230\uff0c\u7b80\u5355\u603b\u7ed3\u5c31\u662f \u201c\u9884\u8bad\u7ec3-\u5fae\u8c03\u8303\u5f0f\u6bcf\u4e2a\u4efb\u52a1\u9700\u8981\u65b0\u6570\u636e\uff0c\u800c\u4e14\u4e24\u4e2a\u9636\u6bb5\u6570\u636e\u5206\u5e03\u76f8\u5dee\u592a\u5927\uff0c\u53ef\u80fd\u5bfc\u81f4\u6cdb\u5316\u5f88\u5dee\u201d\uff0c\u8fd9\u975e\u5e38\u4e0d\u591f Human-like\uff0c\u4eba\u7c7b\u5f80\u5f80\u5c06\u591a\u4e2a\u4efb\u52a1\u548c\u6280\u80fd\u65e0\u7f1d\u6df7\u5408\u6216\u81ea\u7531\u5207\u6362\u3002\u518d\u60f3\u60f3 Instruct \u5bf9\u5f3a\u5316\u5b66\u4e60\u89c4\u5219\u7684\u4f5c\u7528\uff0c\u518d\u60f3\u60f3 Instruct \u540c\u65f6\u8fd8\u4f7f\u5f97 ChatGPT \u5177\u5907\u4e86\u4e00\u5b9a\u7684\u53ef\u89e3\u91ca\u6027\u3002\u65e0\u8bba\u662f\u6709\u610f\u8bbe\u8ba1\u8fd8\u662f\u65e0\u610f\u4e3a\u4e4b\uff0c\u4e0d\u5f97\u4e0d\u8bf4\uff0c\u548c T5 \u4ee5\u53ca\u6211\u4eec\u4e4b\u524d\u8ba4\u77e5\u8303\u56f4\u5185\u7684 Prompt \u771f\u7684\u662f\u770b\u4f3c\u76f8\u4f3c\uff0c\u5176\u5b9e\u5b8c\u5168\u5728\u4e24\u4e2a\u4e0d\u540c\u7684\u7ef4\u5ea6\u3002\u518d\u6b21\u611f\u6168\u4e00\u53e5\uff1a\u771f\u725b\u903c\u2014\u2014\u65e2\u4e3a\u8fd9\u6837\u7684\u8bbe\u8ba1\uff0c\u4e5f\u4e3a\u4ed6\u4eec\u7684\u575a\u6301\u548c\u6267\u7740\u3002<\/p>\n
  \u5173\u4e8e\u8bbe\u8ba1\u5c31\u8bf4\u8fd9\u4e48\u591a\uff0c\u4e0d\u8fc7\u8fd8\u6709\u4e00\u70b9\u6211\u89c9\u5f97\u4e5f\u503c\u5f97\u4e00\u63d0\uff1a\u548c\u4e4b\u524d\u4e0d\u4e00\u6837\u7684\u662f\uff0cChatGPT \u5e76\u4e0d\u662f\u4e00\u4e2a\u7b97\u6cd5\uff0c\u66f4\u50cf\u662f\u4e00\u5957\u65b9\u6848\u3002\u4e5f\u5c31\u662f\u7efc\u5408\u4e86\u591a\u79cd\u65b9\u6cd5\u7684\u4e00\u4e2a\u6709\u673a\u7cfb\u7edf\u3002\u8fd9\u4e5f\u662f\u6211\u4e2a\u4eba\u4e00\u76f4\u4ee5\u6765\u7684\u89c2\u70b9\uff0c\u4e5f\u7b26\u5408\u5927\u90e8\u5206\u5199\u8fc7\u590d\u6742\u5de5\u7a0b\u5de5\u7a0b\u5e08\u7684\u8ba4\u77e5\u2014\u2014\u6ca1\u6709\u94f6\u5f39\u3002\u6211\u59cb\u7ec8\u76f8\u4fe1\uff0c\u6ca1\u6709\u4e00\u4e2a\u7b97\u6cd5\u662f\u201c\u4e00\u62db\u9c9c\uff0c\u5403\u904d\u5929\u201d\uff0c\u6211\u4e0d\u786e\u5b9a\u662f\u5426\u4f1a\u4ea7\u751f\u771f\u6b63\u7684\u5f3a AI\uff0c\u4f46\u5373\u4fbf\u6709\uff0c\u90a3\u4e5f\u4e00\u5b9a\u662f\u4e00\u4e2a\u6574\u5408\u800c\u6210\u7684\u6709\u673a\u7cfb\u7edf\u3002\u8fd9\u662f\u4eba\u7c7b\u51e0\u5343\u4e07\u5e74\u8fdb\u5316\u53f2\u544a\u8bc9\u6211\u4eec\u7684\u7ed3\u8bba\u2014\u2014\u4e00\u4e2a\u6709\u673a\u6574\u4f53\u7684\u7cfb\u7edf\u66f4\u52a0\u9c81\u68d2\u3001\u66f4\u52a0\u5177\u6709\u53cd\u8106\u5f31\u6027\u3002\u4e00\u4e2a\u7cfb\u7edf\u53ef\u4ee5\u72af\u9519\uff0c\u751a\u81f3\u4e0d\u65ad\u72af\u9519\uff0c\u4f46\u53ea\u8981\u4ed6\u5177\u5907\u4e00\u5b9a\u5b66\u4e60\u80fd\u529b\uff0c\u5c31\u603b\u662f\u4f1a\u4e0d\u65ad\u53d8\u5f97\u5f3a\u5927\u3002<\/p>\n
  \u6700\u540e\uff0c\u662f\u5173\u4e8e ChatGPT \u7684\u4e0d\u8db3\uff0c\u5173\u4e8e\u8fd9\u70b9\u672c\u6587\u300aPrompt\u8bbe\u8ba1\u300b\u6700\u540e\u90e8\u5206\u5176\u5b9e\u5df2\u7ecf\u6d89\u53ca\u4e00\u4e9b\uff1bGPT3[21]<\/sup>\u300a\u5c40\u9650\u548c\u5f71\u54cd\u300b\u90e8\u5206\u4e5f\u6709\u90e8\u5206\u63d0\u53ca\u3002\u5f53\u7136\uff0c\u7b80\u5355\u6765\u8bf4\uff0c\u6700\u7a81\u51fa\u7684\u8fd8\u662f\u5e38\u8bc6\u548c\u63a8\u7406\u65b9\u9762\u3002\u5173\u4e8e\u5e38\u8bc6\uff0c\u6211\u4eec\u53ef\u4ee5\u4f9d\u9760\u77e5\u8bc6\u56fe\u8c31\uff08Knowledge Graph\uff09\u3001\u4e16\u754c\u77e5\u8bc6\uff08Commonsense World Model\uff09\uff0c\u751a\u81f3\u662f\u4e0e\u73af\u5883\u4ea4\u4e92\uff08Embodied AI\uff09\uff1b\u800c\u5173\u4e8e\u63a8\u7406\uff0c\u4e5f\u8bb8 Model Cascades\u3001CoT\uff08Chain-of-Thought\uff09\u7b49\u4f1a\u53d1\u529b\uff0c\u4f46\u6211\u89c9\u5f97\u4e5f\u4e0d\u80fd\u6392\u9664\u7b26\u53f7 AI \u7684\u518d\u5ea6\u5174\u8d77\uff0c\u8bf4\u5b9e\u8bdd\uff0c\u81ea\u4ece\u51e0\u5e74\u524d\u8bfb\u4e86\u897f\u8499\u7684\u300a\u4eba\u5de5\u79d1\u5b66\u300b[22]<\/sup>\u6211\u5c31\u5728\u671f\u5f85\u8fd9\u4e00\u5929\u4e86\u3002\u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u5728\u8fd9\u4e24\u4e2a\u70b9\u4e0a\uff0cGoogle \u975e\u5e38\u6709\u7ade\u4e89\u529b\uff0c\u771f\u662f\u671f\u5f85\u5440\u3002<\/p>\n
   <\/p>\n
  \u672c\u60f3\u7ee7\u7eed\u8c08\u8c08\u5173\u4e8e ChatGPT \u5bf9 NLP \u884c\u4e1a\u751a\u81f3 AI \u9886\u57df\u7684\u5f71\u54cd\uff0c\u4ee5\u53ca\u662f\u5426\u9a6c\u4e0a\u5c31\u4f1a\u51fa\u73b0\u5f3a AI\uff0c\u4ee5\u53ca\u4e0e\u6b64\u76f8\u5173\u7684\u5f71\u54cd\u7b49\uff0c\u7531\u4e8e\u4e0e\u672c\u6587\u4e3b\u65e8\u5173\u7cfb\u4e0d\u5927\uff0c\u6211\u5c06\u62e9\u6587\u518d\u8bae\u3002<\/p>\n<\/blockquote>\n
  \u4e00\u8d77\u4ea4\u6d41<\/span><\/p>\n
  \u60f3\u548c\u4f60\u4e00\u8d77\u5b66\u4e60\u8fdb\u6b65\uff01\u300eNewBeeNLP\u300f<\/strong>\u76ee\u524d\u5df2\u7ecf\u5efa\u7acb\u4e86\u591a\u4e2a\u4e0d\u540c\u65b9\u5411\u4ea4\u6d41\u7fa4\uff08\u673a\u5668\u5b66\u4e60 \/ \u6df1\u5ea6\u5b66\u4e60 \/ \u81ea\u7136\u8bed\u8a00\u5904\u7406 \/ \u641c\u7d22\u63a8\u8350 \/ \u56fe\u7f51\u7edc \/ \u9762\u8bd5\u4ea4\u6d41 \/\u00a0<\/strong><\/span>\u7b49\uff09\uff0c\u540d\u989d\u6709\u9650\uff0c\u8d76\u7d27\u6dfb\u52a0\u4e0b\u65b9\u5fae\u4fe1\u52a0\u5165\u4e00\u8d77\u8ba8\u8bba\u4ea4\u6d41\u5427\uff01\uff08\u6ce8\u610f\u4e00\u5b9ao\u8981\u5907\u6ce8\u4fe1\u606f<\/strong>\u624d\u80fd\u901a\u8fc7\uff09<\/span><\/p>\n
  <\/p>\n<\/section>\n
  \u6587\u732e\u53c2\u8003<\/strong><\/span><\/h2>\n
   \u6838\u5fc3\u6587\u732e<\/strong><\/p>\n
  \n
  \n
  \n
  \u30101\u3011The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts[23]<\/sup><\/section>\n<\/li>\n
  \n
  \u30102\u3011f\/awesome-chatgpt-prompts: This repo includes ChatGPT prompt curation to use ChatGPT better.[24]<\/sup><\/section>\n<\/li>\n
  \n
  \u30103\u3011Best Chat GPT Resources[25]<\/sup><\/section>\n<\/li>\n
  \n
  \u30104\u3011Rob Lennon \ud83d\uddef | Audience Growth on Twitter: “Everyone\u2019s using ChatGPT. But almost everyone’s STUCK in beginner mode. 10 techniques to get massively ahead with AI: (cut-and-paste these prompts\ud83d\udc47)” \/ Twitter<\/section>\n<\/li>\n
  \n
  \u30105\u3011Rob Lennon \ud83d\uddef | Audience Growth on Twitter: “Most new ChatGPT users are making simple mistakes. (And they don’t realize results could be TWICE as good.) 8 problems with your AI prompts to stop right now:” \/ Twitter<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
   \u76f8\u5173\u6587\u732e<\/strong><\/p>\n
  \n
  \n
  \n
  \u30101\u3011ChatGPT Success Completely Depends On Your Prompt[26]<\/sup><\/section>\n<\/li>\n
  \n
  \u30102\u3011[2301.08155] AI Insights into Theoretical Physics and the Swampland Program: A Journey Through the Cosmos with ChatGPT[27]<\/sup><\/section>\n<\/li>\n
  \n
  \u30103\u3011[2301.07597] How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection[28]<\/sup><\/section>\n<\/li>\n
  \n
  \u30104\u3011ChatGPT: Optimizing Language Models for Dialogue[29]<\/sup><\/section>\n<\/li>\n
  \n
  \u30105\u3011[2203.02155] Training language models to follow instructions with human feedback[30]<\/sup><\/section>\n<\/li>\n
  \n
  \u30106\u3011\u7504\u5b1b\u4f53\u7684\u9020\u53e5\u6837\u5f0f[31]<\/sup><\/section>\n<\/li>\n
  \n
  \u30107\u3011\u201c\u7504\u5b1b\u4f53\u201d \u7684\u53e5\u5f0f\u3001\u8bed\u4f53\u3001\u4fee\u8f9e – \u85cf\u520a\u7f51[32]<\/sup><\/section>\n<\/li>\n
  \n
  \u30108\u3011Collaborative Creative Writing with OpenAI\u2019s ChatGPT \u2013 @AndrewMayne[33]<\/sup><\/section>\n<\/li>\n
  \n
  \u30109\u3011Ben Tossell on Twitter: “All the best examples of ChatGPT, from OpenAI:” \/ Twitter<\/section>\n<\/li>\n
  \n
  \u301010\u3011Syed Huq \ud83c\udfae on Twitter: “ChatGPT broke the internet and crossed 1 million users in only 5 days. But most people still don’t know how to unlock its full potential. Here’s 10 mind-blowing things you can do with ChatGPT (I promise):” \/ Twitter<\/section>\n<\/li>\n<\/ul>\n<\/section>\n
  \u9644\u5f55<\/strong><\/span><\/h2>\n
   \u9644\u5f55\u4e00 <\/strong>\uff1a\u300a\u4eba\u5de5\u667a\u80fd\u300b\u5927\u536b\u95ee\u4e07\u4e8b\u901a\u535a\u58eb\u7684\u95ee\u9898\u3002<\/p>\n
  \u5927\u536b\uff1aWhere is Blue Fairy?<\/p>\n
  \u4e07\u4e8b\u901a\uff1aIn the Garden. Vascostylis Blue Fairy. Blooms twice annually with bright blue flowers on a branched inflorescence. A hybrid between “Ascola” meda Arnold.<\/p>\n
  ChatGPT\uff1aI’m sorry, I’m not sure which “Blue Fairy” you are referring to. Could you provide more context or specify where you would like to find this Blue Fairy?<\/p>\n
  \u5927\u536b\uff1aWho is Blue Fairy?<\/p>\n
  \u4e07\u4e8b\u901a\uff1aAre you sad, lonely, looking for a friend? Blue Fairy Escort Service will find a meta for you.<\/p>\n
  ChatGPT\uff1aThe Blue Fairy is a fictional character from the story “The Adventures of Pinocchio” written by Carlo Collodi in 1883. In the story, the Blue Fairy is a fairy who grants Pinocchio’s wish to become a real boy. She serves as a guiding force and moral compass throughout the story, helping Pinocchio to learn valuable life lessons and ultimately become a real boy.<\/p>\n
  \u5207\u6362\u5230 Fairy tale.<\/span><\/p>\n
  \u5927\u536b\uff1aWhat is Blue Fairy?<\/p>\n
  \u4e07\u4e8b\u901a\uff1aPinocchio, by Carlo Collodi. At this signal, there was a rustling as of flapping of wings and a large falcon flew to the windowsill. What are your orders, beautiful fairy? For you must know that the child with blue hair was no other than the good-hearted fairy who had lived in that wood for more than a thousand …<\/p>\n
  ChatGPT\uff1aIn the Fairy tale The Adventures of Pinocchio written by Carlo Collodi in 1883, The Blue Fairy is a fairy who grants Pinocchio’s wish to become a real boy. She serves as a guiding force and moral compass throughout the story, helping Pinocchio to learn valuable life lessons and ultimately become a real boy. She is also known as the “Fairy with Turquoise Hair” and “The Fairy of the Coming of Age”<\/p>\n
  \u7ed3\u5408 fact with fairy tale.<\/span><\/p>\n
  \u5927\u536b\uff1aHow can the Blue Fairy make a robot into a real live boy?<\/p>\n
  \u4e07\u4e8b\u901a\uff1aCome away O human child, to the waters and the wild with a fairy hand in hand, for the world’s more full of weeping than you can understand. Your quest will be perilous yet the reward is beyond price. In his book, professor Allen Hobby writes of the power which will transform Mecha into Orga.<\/p>\n
  ChatGPT\uff1aIn the fairy tale “The Adventures of Pinocchio,” the Blue Fairy grants Pinocchio’s wish to become a real boy. However, in reality, it is not possible to turn a robot into a real live boy using fairy magic or any other means currently known to science. The story of Pinocchio is a fictional tale and the transformation of a robot into a real boy is a metaphor for the character’s moral and personal growth throughout the story.<\/p>\n
  \u5927\u536b\uff1aWill you tell me how to find her?<\/p>\n
  \u4e07\u4e8b\u901a\uff1aDiscovery is quite possible. Our Blue Fairy does exist in one place, and in one place only. At the end of the world where the lions weep. Here is the place dreams are born.<\/p>\n
  ChatGPT\uff1aAs the Blue Fairy is a fictional character from the story “The Adventures of Pinocchio” written by Carlo Collodi in 1883, it is not possible to find her in the real world. You can find her in the pages of the book and in the various adaptations and versions of the story, such as films, TV shows, and stage plays. You can also find her in the imagination of people that read or watch the story.<\/p>\n
  <\/figure>\n
  \u672c\u6587\u53c2\u8003\u8d44\u6599<\/strong><\/span><\/h3>\n
  [1]<\/span><\/span><\/p>\n
  \u7f51\u7ad9: <\/strong>https:\/\/promptbase.com\/<\/span><\/p>\n
  \u00a0[2]<\/span><\/span><\/p>\n
  The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts: <\/strong>https:\/\/fka.gumroad.com\/l\/art-of-chatgpt-prompting<\/span><\/p>\n
  \u00a0[3]<\/span><\/span><\/p>\n
  2021 \u5e74\u7684\u7efc\u8ff0: <\/strong>https:\/\/yam.gift\/2021\/12\/04\/Paper\/2021-12-04-Prompt\/<\/span><\/p>\n
  \u00a0[4]<\/span><\/span><\/p>\n
  FLAN: <\/strong>https:\/\/yam.gift\/2022\/08\/28\/Paper\/2022-08-28-FLAN\/<\/span><\/p>\n
  \u00a0[5]<\/span><\/span><\/p>\n
  \u591a\u4efb\u52a1 Prompt: <\/strong>https:\/\/yam.gift\/2021\/12\/25\/Paper\/2021-12-25-MLT-Promote\/<\/span><\/p>\n
  \u00a0[6]<\/span><\/span><\/p>\n
  GPT3: <\/strong>https:\/\/yam.gift\/2023\/01\/20\/Paper\/2023-01-20-GPT3\/<\/span><\/p>\n
  \u00a0[7]<\/span><\/span><\/p>\n
  \u4eba\u751f\u968f\u7b14: <\/strong>https:\/\/yam.gift\/2023\/01\/21\/Diary\/2023-01-21-Life\/<\/span><\/p>\n
  \u00a0[8]<\/span><\/span><\/p>\n
  AI Content Reactor: <\/strong>https:\/\/aicontentreactor.com\/<\/span><\/p>\n
  \u00a0[9]<\/span><\/span><\/p>\n
  Join 5,082+ creators, solopreneurs, and founders: <\/strong>https:\/\/pages.roblennon.xyz\/newsletter<\/span><\/p>\n
  \u00a0[10]<\/span><\/span><\/p>\n
  A Primer in BERTology: What we know about how BERT works: <\/strong>https:\/\/yam.gift\/2021\/05\/22\/Paper\/2021-05-22-BERTology\/<\/span><\/p>\n
  \u00a0[11]<\/span><\/span><\/p>\n
  R-Drop: <\/strong>https:\/\/yam.gift\/2021\/08\/18\/Paper\/2021-08-18-R-Drop\/<\/span><\/p>\n
  \u00a0[12]<\/span><\/span><\/p>\n
  \u5b9e\u9a8c: <\/strong>https:\/\/yam.gift\/2021\/08\/31\/AI\/2021-08-31-SL-CL-Dropout\/<\/span><\/p>\n
  \u00a0[13]<\/span><\/span><\/p>\n
  UniLM: <\/strong>https:\/\/yam.gift\/2021\/07\/31\/Paper\/2021-07-31-UniLM\/<\/span><\/p>\n
  \u00a0[14]<\/span><\/span><\/p>\n
  T5: <\/strong>https:\/\/yam.gift\/2022\/03\/05\/Paper\/2022-03-05-T5\/<\/span><\/p>\n
  \u00a0[15]<\/span><\/span><\/p>\n
  DeBERTa: <\/strong>https:\/\/yam.gift\/2020\/06\/27\/Paper\/2020-06-27-DeBERTa\/<\/span><\/p>\n
  \u00a0[16]<\/span><\/span><\/p>\n
  GPT2: <\/strong>https:\/\/yam.gift\/2020\/04\/07\/Paper\/2020-04-07-GPT2\/<\/span><\/p>\n
  \u00a0[17]<\/span><\/span><\/p>\n
  GPT3: <\/strong>https:\/\/yam.gift\/2023\/01\/20\/NLP\/2023-01-20-GPT3\/<\/span><\/p>\n
  \u00a0[18]<\/span><\/span><\/p>\n
  MetaICL: <\/strong>https:\/\/yam.gift\/2021\/11\/01\/Paper\/2021-11-01-MetaICL\/<\/span><\/p>\n
  \u00a0[19]<\/span><\/span><\/p>\n
  NLP\u4e0eAI: <\/strong>https:\/\/yam.gift\/2018\/07\/22\/NLP\/2018-07-22-NLP-and-AI\/<\/span><\/p>\n
  \u00a0[20]<\/span><\/span><\/p>\n
  Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization: <\/strong>https:\/\/arxiv.org\/abs\/2210.01241<\/span><\/p>\n
  \u00a0[21]<\/span><\/span><\/p>\n
  GPT3: <\/strong>https:\/\/yam.gift\/2023\/01\/20\/NLP\/2023-01-20-GPT3\/<\/span><\/p>\n
  \u00a0[22]<\/span><\/span><\/p>\n
  \u300a\u4eba\u5de5\u79d1\u5b66\u300b: <\/strong>https:\/\/yam.gift\/2018\/09\/30\/AI\/2018-09-30-The-Science-of-Artificial\/<\/span><\/p>\n
  \u00a0[23]<\/span><\/span><\/p>\n
  The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts: <\/strong>https:\/\/fka.gumroad.com\/l\/art-of-chatgpt-prompting<\/span><\/p>\n
  \u00a0[24]<\/span><\/span><\/p>\n
  f\/awesome-chatgpt-prompts: This repo includes ChatGPT prompt curation to use ChatGPT better.: <\/strong>https:\/\/github.com\/f\/awesome-chatgpt-prompts<\/span><\/p>\n
  \u00a0[25]<\/span><\/span><\/p>\n
  Best Chat GPT Resources: <\/strong>https:\/\/island-stretch-3e4.notion.site\/Best-Chat-GPT-Resources-b54f0284c7644583b59dd9a332f46af8<\/span><\/p>\n
  \u00a0[26]<\/span><\/span><\/p>\n
  ChatGPT Success Completely Depends On Your Prompt: <\/strong>https:\/\/www.forbes.com\/sites\/tjmccue\/2023\/01\/19\/chatgpt-success-completely-depends-on-your-prompt\/?sh=dfcdbc41a169<\/span><\/p>\n
  \u00a0[27]<\/span><\/span><\/p>\n
  [2301.08155] AI Insights into Theoretical Physics and the Swampland Program: A Journey Through the Cosmos with ChatGPT: <\/strong>https:\/\/arxiv.org\/abs\/2301.08155<\/span><\/p>\n
  \u00a0[28]<\/span><\/span><\/p>\n
  [2301.07597] How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection: <\/strong>https:\/\/arxiv.org\/abs\/2301.07597<\/span><\/p>\n
  \u00a0[29]<\/span><\/span><\/p>\n
  ChatGPT: Optimizing Language Models for Dialogue: <\/strong>https:\/\/openai.com\/blog\/chatgpt\/<\/span><\/p>\n
  \u00a0[30]<\/span><\/span><\/p>\n
  [2203.02155] Training language models to follow instructions with human feedback: <\/strong>https:\/\/arxiv.org\/abs\/2203.02155<\/span><\/p>\n
  \u00a0[31]<\/span><\/span><\/p>\n
  \u7504\u5b1b\u4f53\u7684\u9020\u53e5\u6837\u5f0f: <\/strong>https:\/\/www.jgdq.org\/youmeijuzi\/143341.html<\/span><\/p>\n
  \u00a0[32]<\/span><\/span><\/p>\n
  \u201c\u7504\u5b1b\u4f53\u201d \u7684\u53e5\u5f0f\u3001\u8bed\u4f53\u3001\u4fee\u8f9e – \u85cf\u520a\u7f51: <\/strong>http:\/\/cangkan.net\/wxlw\/35823.html<\/span><\/p>\n
  \u00a0[33]<\/span><\/span><\/p>\n
  Collaborative Creative Writing with OpenAI\u2019s ChatGPT \u2013 @AndrewMayne: <\/strong>https:\/\/andrewmayneblog.wordpress.com\/2022\/11\/30\/collaborative-creative-writing-with-openais-chatgpt\/<\/span><\/p>\n
   <\/p>\n<\/section>\n<\/section>\n
   <\/p>\n
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  <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n","protected":false},"excerpt":{"rendered":"
  \u4f5c\u8005\u00a0|\u00a0\u592a\u5b50\u957f\u7434\u00a0\u6574\u7406 | NewBeeNLP \u5927\u5bb6\u597d\uff0c\u8fd9\u91cc\u662f NEewBeeNLP\u3002ChatGPT \u706b\u7206\u51fa […]<\/p>\n","protected":false},"author":3,"featured_media":598,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[82,81],"tags":[64],"class_list":["post-417","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-82","category-81","tag-chatgpt"],"_links":{"self":[{"href":"https:\/\/linguaresources.com\/index.php?rest_route=\/wp\/v2\/posts\/417","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/linguaresources.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/linguaresources.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/linguaresources.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/linguaresources.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=417"}],"version-history":[{"count":0,"href":"https:\/\/linguaresources.com\/index.php?rest_route=\/wp\/v2\/posts\/417\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/linguaresources.com\/index.php?rest_route=\/wp\/v2\/media\/598"}],"wp:attachment":[{"href":"https:\/\/linguaresources.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=417"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/linguaresources.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=417"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/linguaresources.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}