{"id":297,"date":"2026-05-26T07:03:40","date_gmt":"2026-05-25T23:03:40","guid":{"rendered":"http:\/\/climbing.top\/index.php\/2026\/05\/26\/tishigongchengprompt-engineering\/"},"modified":"2026-05-26T07:03:40","modified_gmt":"2026-05-25T23:03:40","slug":"tishigongchengprompt-engineering","status":"publish","type":"post","link":"https:\/\/climbing.top\/index.php\/2026\/05\/26\/tishigongchengprompt-engineering\/","title":{"rendered":"\u63d0\u793a\u5de5\u7a0b\uff08Prompt Engineering\uff09"},"content":{"rendered":"
\n

\u539f\u6587\u4f5c\u8005\uff1aLilian Weng | \u539f\u6587\u65e5\u671f\uff1a2023\u5e743\u670815\u65e5 | \u8bd1\u8005\uff1a\u6500\u5ca9\u8005<\/p>\n<\/blockquote>\n

\n

\u539f\u6587\u94fe\u63a5\uff1ahttps:\/\/lilianweng.github.io\/posts\/2023-03-15-prompt-engineering\/<\/p>\n<\/blockquote>\n

\n

\u5f15\u8a00<\/h2>\n

\u63d0\u793a\u5de5\u7a0b\uff08Prompt Engineering\uff09<\/strong>\uff0c\u4e5f\u79f0\u4e3a\u4e0a\u4e0b\u6587\u63d0\u793a\uff08In-Context Prompting\uff09<\/strong>\uff0c\u662f\u6307\u5728\u4e0d\u66f4\u65b0\u6a21\u578b\u6743\u91cd<\/strong>\u7684\u524d\u63d0\u4e0b\uff0c\u901a\u8fc7\u8bbe\u8ba1\u8f93\u5165\u63d0\u793a\u6765\u5f15\u5bfc\u5927\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u4ea7\u751f\u671f\u671b\u8f93\u51fa\u7684\u65b9\u6cd5\u3002\u5b83\u672c\u8d28\u4e0a\u662f\u4e00\u95e8\u5b9e\u9a8c\u79d1\u5b66\u2014\u2014\u540c\u6837\u7684\u63d0\u793a\u6280\u5de7\u5728\u4e0d\u540c\u6a21\u578b\u4e0a\u6548\u679c\u53ef\u80fd\u5929\u5dee\u5730\u522b\uff0c\u56e0\u6b64\u9700\u8981\u5927\u91cf\u7684\u5b9e\u9a8c\u548c\u7ecf\u9a8c\u79ef\u7d2f\u3002<\/p>\n

\u672c\u6587\u805a\u7126\u4e8e\u81ea\u56de\u5f52\u8bed\u8a00\u6a21\u578b\u7684\u63d0\u793a\u5de5\u7a0b\uff0c\u4e0d\u6d89\u53ca\u5b8c\u5f62\u586b\u7a7a\u3001\u56fe\u50cf\u751f\u6210\u6216\u591a\u6a21\u6001\u6a21\u578b\u3002\u63d0\u793a\u5de5\u7a0b\u7684\u6838\u5fc3\u76ee\u6807\u662f\u5bf9\u9f50\uff08Alignment\uff09<\/strong>\u4e0e\u53ef\u63a7\u751f\u6210\uff08Steerability\uff09<\/strong>\u3002<\/p>\n

\n

\ud83d\udca1 Lilian \u7684\u4e2a\u4eba\u89c2\u70b9<\/strong>\uff1a\u6709\u4e9b\u63d0\u793a\u5de5\u7a0b\u8bba\u6587\u5199\u4e868\u9875\uff0c\u5176\u5b9e\u6838\u5fc3\u6280\u5de7\u4e00\u4e24\u53e5\u8bdd\u5c31\u80fd\u8bf4\u6e05\uff0c\u5269\u4e0b\u7684\u5168\u662f\u8dd1 benchmark\u3002\u793e\u533a\u66f4\u9700\u8981\u597d\u7528\u7684\u5171\u4eab\u8bc4\u6d4b\u57fa\u7840\u8bbe\u65bd\u3002<\/p>\n<\/blockquote>\n

\u3010\u9605\u8bfb\u603b\u7ed3\u3011\u63d0\u793a\u5de5\u7a0b\u662f\u5728\u4e0d\u4fee\u6539\u6a21\u578b\u53c2\u6570\u7684\u60c5\u51b5\u4e0b\uff0c\u901a\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u8f93\u5165\u6587\u672c\u6765\u5f15\u5bfc LLM \u8f93\u51fa\u7684\u4e00\u95e8\u5b9e\u9a8c\u6027\u6280\u672f\uff0c\u6838\u5fc3\u76ee\u6807\u662f\u5bf9\u9f50\u548c\u53ef\u63a7\u3002<\/p>\n

<\/p>\n

\n

\u4e00\u3001\u57fa\u7840\u63d0\u793a\u65b9\u6cd5\uff08Basic Prompting\uff09<\/h2>\n

1.1 \u96f6\u6837\u672c\uff08Zero-Shot\uff09<\/h3>\n

\u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-shot Learning\uff09\u6700\u7b80\u5355\u2014\u2014\u76f4\u63a5\u628a\u4efb\u52a1\u6587\u672c\u5582\u7ed9\u6a21\u578b\uff0c\u8ba9\u5b83\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n

`<\/code><\/p>\n

Text: i’ll bet the video game is a lot more fun than the film.<\/p>\n

Sentiment:<\/p>\n

`<\/code><\/p>\n

\u6a21\u578b\u9700\u8981\u81ea\u884c\u7406\u89e3”Sentiment”\u610f\u5473\u7740\u60c5\u611f\u5206\u7c7b\uff0c\u7136\u540e\u7ed9\u51fa\u7b54\u6848\u3002<\/p>\n

1.2 \u5c11\u6837\u672c\uff08Few-Shot\uff09<\/h3>\n

\u5c11\u6837\u672c\u5b66\u4e60\uff08Few-shot Learning\uff09\u5728\u63d0\u793a\u4e2d\u7ed9\u51fa\u82e5\u5e72\u9ad8\u8d28\u91cf\u793a\u4f8b\uff0c\u6bcf\u4e2a\u793a\u4f8b\u5305\u542b\u8f93\u5165\u548c\u671f\u671b\u8f93\u51fa\uff1a<\/p>\n

`<\/code><\/p>\n

Text: (lawrence bounces) all over the stage, dancing, running, sweating…<\/p>\n

Sentiment: positive<\/p>\n

Text: despite all evidence to the contrary, this clunker has somehow managed to pose as an actual feature movie…<\/p>\n

Sentiment: negative<\/p>\n

Text: for the first time in years, de niro digs deep emotionally…<\/p>\n

Sentiment: positive<\/p>\n

Text: i’ll bet the video game is a lot more fun than the film.<\/p>\n

Sentiment:<\/p>\n

`<\/code><\/p>\n

\u5c11\u6837\u672c\u901a\u5e38\u6bd4\u96f6\u6837\u672c\u6548\u679c\u597d\uff0c\u4f46\u4ee3\u4ef7\u662f\u6d88\u8017\u66f4\u591a token\uff0c\u4e14\u53ef\u80fd\u89e6\u53ca\u4e0a\u4e0b\u6587\u957f\u5ea6\u9650\u5236\u3002<\/p>\n

\u7814\u7a76\u53d1\u73b0\uff0c\u63d0\u793a\u683c\u5f0f\u3001\u793a\u4f8b\u9009\u62e9\u548c\u793a\u4f8b\u6392\u5e8f<\/strong>\u90fd\u53ef\u80fd\u5bfc\u81f4\u6027\u80fd\u5929\u58e4\u4e4b\u522b\u2014\u2014\u4ece\u8fd1\u4e4e\u968f\u673a\u731c\u5230\u63a5\u8fd1 SOTA\u3002<\/p>\n

\u5173\u952e\u504f\u5dee\u95ee\u9898<\/strong>\uff08Zhao et al., 2021\uff09\uff1a<\/p>\n

    \n
  • \u591a\u6570\u6807\u7b7e\u504f\u5dee\uff08Majority Label Bias\uff09<\/strong>\uff1a\u793a\u4f8b\u4e2d\u6807\u7b7e\u5206\u5e03\u4e0d\u5747<\/li>\n
  • \u8fd1\u56e0\u504f\u5dee\uff08Recency Bias\uff09<\/strong>\uff1a\u6a21\u578b\u503e\u5411\u4e8e\u91cd\u590d\u6700\u540e\u4e00\u4e2a\u6807\u7b7e<\/li>\n
  • \u5e38\u89c1 token \u504f\u5dee\uff08Common Token Bias\uff09<\/strong>\uff1a\u6a21\u578b\u504f\u597d\u751f\u6210\u5e38\u89c1 token<\/li>\n<\/ul>\n

    1.3 \u793a\u4f8b\u9009\u62e9\u6280\u5de7<\/h3>\n
      \n
    • \u4f7f\u7528\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684 k-NN<\/strong> \u9009\u62e9\u8bed\u4e49\u76f8\u4f3c\u7684\u793a\u4f8b\uff08Liu et al., 2021\uff09<\/li>\n
    • \u57fa\u4e8e\u56fe\u7684\u591a\u6837\u6027\u9009\u62e9<\/strong>\uff1a\u6784\u5efa\u6709\u5411\u56fe\uff0c\u9f13\u52b1\u9009\u62e9\u591a\u6837\u5316\u7684\u793a\u4f8b\uff08Su et al., 2022\uff09<\/li>\n
    • \u7528\u5bf9\u6bd4\u5b66\u4e60\uff08Contrastive Learning\uff09<\/strong>\u8bad\u7ec3\u4e13\u95e8\u7684\u5d4c\u5165\u6765\u9009\u62e9\u793a\u4f8b\uff08Rubin et al., 2022\uff09<\/li>\n
    • \u57fa\u4e8e\u4e0d\u786e\u5b9a\u6027<\/strong>\u7684\u4e3b\u52a8\u5b66\u4e60\u7b56\u7565\uff0c\u9009\u62e9\u6a21\u578b\u5206\u6b67\u5927\u7684\u6837\u672c\uff08Diao et al., 2023\uff09<\/li>\n<\/ul>\n

      1.4 \u793a\u4f8b\u6392\u5e8f\u6280\u5de7<\/h3>\n
        \n
      • \u4fdd\u6301\u793a\u4f8b\u591a\u6837\u5316\u3001\u4e0e\u6d4b\u8bd5\u6837\u672c\u76f8\u5173\u3001\u968f\u673a\u6392\u5e8f\u4ee5\u907f\u514d\u504f\u5dee<\/li>\n
      • \u589e\u5927\u6a21\u578b\u6216\u589e\u52a0\u793a\u4f8b\u6570\u5e76\u4e0d\u80fd\u51cf\u5c11\u6392\u5e8f\u5e26\u6765\u7684\u65b9\u5dee\uff08Lu et al., 2022\uff09<\/li>\n
      • \u9a8c\u8bc1\u96c6\u6709\u9650\u65f6\uff0c\u9009\u62e9\u4f7f\u6a21\u578b\u9884\u6d4b\u4e0d\u8fc7\u4e8e\u5931\u8861\u7684\u6392\u5e8f<\/li>\n<\/ul>\n

        \u3010\u9605\u8bfb\u603b\u7ed3\u3011\u96f6\u6837\u672c\u6700\u7b80\u5355\uff0c\u5c11\u6837\u672c\u901a\u8fc7\u793a\u4f8b\u5f15\u5bfc\u6548\u679c\u66f4\u597d\uff0c\u4f46\u793a\u4f8b\u7684\u9009\u62e9\u548c\u6392\u5e8f\u90fd\u4f1a\u663e\u8457\u5f71\u54cd\u7ed3\u679c\uff0c\u9700\u8981\u6ce8\u610f\u5404\u79cd\u504f\u5dee\u95ee\u9898\u3002<\/p>\n

        \n

        \u4e8c\u3001\u6307\u4ee4\u63d0\u793a\uff08Instruction Prompting\uff09<\/h2>\n

        \u5c11\u6837\u672c\u793a\u4f8b\u7684\u672c\u8d28\u662f\u901a\u8fc7”\u6f14\u793a”\u6765\u4f20\u8fbe\u4efb\u52a1\u6307\u4ee4\u3002\u4f46\u65e2\u7136\u5982\u6b64\uff0c\u4e3a\u4ec0\u4e48\u4e0d\u76f4\u63a5\u5199\u6307\u4ee4\u5462\uff1f<\/strong><\/p>\n

        \u7ecf\u8fc7\u6307\u4ee4\u5fae\u8c03\u7684\u6a21\u578b\uff08\u5982 InstructGPT\uff09\u901a\u8fc7 RLHF\uff08\u57fa\u4e8e\u4eba\u7c7b\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60\uff09\u8bad\u7ec3\uff0c\u80fd\u66f4\u597d\u5730\u7406\u89e3\u7528\u6237\u610f\u56fe\u3002\u4e0e\u8fd9\u7c7b\u6a21\u578b\u4ea4\u4e92\u65f6\uff0c\u5173\u952e\u539f\u5219\u662f\uff1a\u5177\u4f53\u3001\u7cbe\u786e\u3001\u544a\u8bc9\u6a21\u578b”\u505a\u4ec0\u4e48”\u800c\u4e0d\u662f”\u4e0d\u505a\u4ec0\u4e48”<\/strong>\u3002<\/p>\n

        `<\/code><\/p>\n

        Please label the sentiment towards the movie of the given movie review. <\/p>\n

        The sentiment label should be “positive” or “negative”. <\/p>\n

        Text: i’ll bet the video game is a lot more fun than the film. <\/p>\n

        Sentiment:<\/p>\n

        `<\/code><\/p>\n

        \u6307\u5b9a\u53d7\u4f17<\/strong>\u4e5f\u662f\u597d\u6280\u5de7\uff1a<\/p>\n

          \n
        • “\u75286\u5c81\u5c0f\u5b69\u80fd\u61c2\u7684\u8bdd\u89e3\u91ca\u91cf\u5b50\u7269\u7406”<\/li>\n
        • “\u7528\u9002\u5408\u804c\u573a\u7684\u8bed\u8a00\u6765\u56de\u7b54”<\/li>\n<\/ul>\n

          \u4e0a\u4e0b\u6587\u6307\u4ee4\u5b66\u4e60\uff08In-Context Instruction Learning\uff09<\/strong>\uff08Ye et al., 2023\uff09\u5c06\u5c11\u6837\u672c\u4e0e\u6307\u4ee4\u7ed3\u5408\uff0c\u5728\u63d0\u793a\u4e2d\u5305\u542b\u591a\u4e2a\u4e0d\u540c\u4efb\u52a1\u7684\u793a\u4f8b\uff0c\u6bcf\u4e2a\u793a\u4f8b\u5305\u542b\u6307\u4ee4\u3001\u8f93\u5165\u548c\u8f93\u51fa\u3002<\/p>\n

          \u3010\u9605\u8bfb\u603b\u7ed3\u3011\u5bf9\u6307\u4ee4\u5fae\u8c03\u6a21\u578b\uff0c\u76f4\u63a5\u5199\u6e05\u695a\u4efb\u52a1\u8981\u6c42\u6bd4\u7ed9\u793a\u4f8b\u66f4\u9ad8\u6548\uff0c\u5173\u952e\u662f\u5177\u4f53\u3001\u7cbe\u786e\u3001\u6b63\u9762\u63cf\u8ff0\u3002<\/p>\n

          \n

          \u4e09\u3001\u81ea\u4e00\u81f4\u6027\u91c7\u6837\uff08Self-Consistency Sampling\uff09<\/h2>\n

          \u81ea\u4e00\u81f4\u6027\u91c7\u6837\uff08Wang et al., 2022a\uff09\u7684\u6838\u5fc3\u601d\u8def\uff1a\u7528 temperature > 0 \u591a\u6b21\u91c7\u6837\uff0c\u7136\u540e\u4ece\u5019\u9009\u4e2d\u9009\u6700\u4f18\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u591a\u6570\u6295\u7968\uff08Majority Vote\uff09<\/strong>\u3002\u5bf9\u4e8e\u5bb9\u6613\u9a8c\u8bc1\u7684\u4efb\u52a1\uff08\u5982\u7f16\u7a0b\u9898\uff09\uff0c\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884c\u5355\u5143\u6d4b\u8bd5\u6765\u5224\u65ad\u3002<\/p>\n

          \u3010\u9605\u8bfb\u603b\u7ed3\u3011\u591a\u6b21\u91c7\u6837\u53d6\u591a\u6570\u6295\u7968\uff0c\u7b80\u5355\u6709\u6548\u63d0\u5347\u63a8\u7406\u51c6\u786e\u7387\u3002<\/p>\n

          \n

          \u56db\u3001\u601d\u7ef4\u94fe\uff08Chain-of-Thought, CoT\uff09<\/h2>\n

          4.1 \u4ec0\u4e48\u662f CoT<\/h3>\n

          \u601d\u7ef4\u94fe\u63d0\u793a\uff08Wei et al., 2022\uff09\u8ba9\u6a21\u578b\u751f\u6210\u4e00\u7cfb\u5217\u77ed\u53e5\uff0c\u9010\u6b65\u63cf\u8ff0\u63a8\u7406\u8fc7\u7a0b<\/strong>\uff08\u79f0\u4e3a\u63a8\u7406\u94fe\uff09\uff0c\u6700\u7ec8\u5f97\u51fa\u7b54\u6848\u3002CoT \u5728\u590d\u6742\u63a8\u7406\u4efb\u52a1<\/strong>\u548c\u5927\u6a21\u578b<\/strong>\uff0850B+ \u53c2\u6570\uff09\u4e0a\u6548\u679c\u663e\u8457\u3002<\/p>\n

          4.2 CoT \u7684\u4e24\u79cd\u7c7b\u578b<\/h3>\n

          \u5c11\u6837\u672c CoT<\/strong>\u2014\u2014\u5728\u793a\u4f8b\u4e2d\u624b\u5199\u63a8\u7406\u8fc7\u7a0b\uff1a<\/p>\n

          `<\/code><\/p>\n

          Question: Tom and Elizabeth have a competition to climb a hill. <\/p>\n

          Elizabeth takes 30 minutes. Tom takes four times as long. <\/p>\n

          How many hours does it take Tom?<\/p>\n

          Answer: It takes Tom 30*4 = 120 minutes to climb the hill.<\/p>\n

          It takes Tom 120\/60 = 2 hours to climb the hill.<\/p>\n

          So the answer is 2.<\/p>\n

          `<\/code><\/p>\n

          \u96f6\u6837\u672c CoT<\/strong>\u2014\u2014\u53ea\u9700\u52a0\u4e00\u53e5”Let’s think step by step<\/strong>“\uff1a<\/p>\n

          `<\/code><\/p>\n

          Question: Marty has 100cm of ribbon, cut into 4 equal parts, <\/p>\n

          each divided into 5. How long is each final cut?<\/p>\n

          Answer: Let’s think step by step.<\/p>\n

          `<\/code><\/p>\n

          4.3 CoT \u6269\u5c55\u6280\u5de7<\/h3>\n
            \n
          • \u81ea\u4e00\u81f4\u6027\u91c7\u6837<\/strong>\u53ef\u8fdb\u4e00\u6b65\u63d0\u5347\u63a8\u7406\u51c6\u786e\u7387\uff08Wang et al., 2022a\uff09<\/li>\n
          • STaR\uff08\u81ea\u6559\u63a8\u7406\u5668\uff09<\/strong>\uff1a\u8ba9\u6a21\u578b\u751f\u6210\u63a8\u7406\u94fe\uff0c\u4fdd\u7559\u6b63\u786e\u7684\uff0c\u5fae\u8c03\u540e\u8fed\u4ee3\uff08Zelikman et al., 2022\uff09<\/li>\n
          • \u63a8\u7406\u6b65\u9aa4\u8d8a\u591a\u3001\u590d\u6742\u5ea6\u8d8a\u9ad8\uff0c\u6548\u679c\u8d8a\u597d\uff1b\u6362\u884c\u7b26 n<\/code> \u5206\u9694\u4f18\u4e8e\u53e5\u53f7\u6216\u5206\u53f7\uff08Fu et al., 2023\uff09<\/li>\n
          • \u57fa\u4e8e\u590d\u6742\u5ea6\u7684\u4e00\u81f4\u6027<\/strong>\uff1a\u53ea\u5728\u6700\u590d\u6742\u7684\u63a8\u7406\u94fe\u4e2d\u505a\u591a\u6570\u6295\u7968<\/li>\n
          • Self-Ask<\/strong>\uff1a\u8ba9\u6a21\u578b\u53cd\u590d\u8ffd\u95ee\u81ea\u5df1\uff0c\u6784\u5efa\u8fed\u4ee3\u63a8\u7406\u8fc7\u7a0b\uff08Press et al., 2022\uff09<\/li>\n
          • ReAct<\/strong>\uff1a\u7ed3\u5408\u63a8\u7406\u4e0e\u884c\u52a8\uff0c\u4ea4\u66ff\u8c03\u7528\u641c\u7d22\u5f15\u64ce\u83b7\u53d6\u4fe1\u606f\uff08Yao et al., 2023\uff09<\/li>\n
          • \u601d\u7ef4\u6811\uff08Tree of Thoughts\uff09<\/strong>\uff1a\u5728\u6bcf\u4e00\u6b65\u63a2\u7d22\u591a\u4e2a\u63a8\u7406\u5206\u652f\uff0c\u7528 BFS\/DFS \u641c\u7d22\u6700\u4f18\u8def\u5f84\uff08Yao et al., 2023\uff09<\/li>\n<\/ul>\n

            \u3010\u9605\u8bfb\u603b\u7ed3\u3011CoT \u901a\u8fc7\u8ba9\u6a21\u578b”\u5c55\u793a\u601d\u8003\u8fc7\u7a0b”\u5927\u5e45\u63d0\u5347\u590d\u6742\u63a8\u7406\u80fd\u529b\uff0c\u914d\u5408\u81ea\u4e00\u81f4\u6027\u91c7\u6837\u3001\u601d\u7ef4\u6811\u7b49\u6280\u672f\u53ef\u4ee5\u8fdb\u4e00\u6b65\u589e\u5f3a\u6548\u679c\u3002<\/p>\n

            \n

            \u4e94\u3001\u81ea\u52a8\u63d0\u793a\u8bbe\u8ba1\uff08Automatic Prompt Design\uff09<\/h2>\n

            \u63d0\u793a\u53ef\u4ee5\u88ab\u89c6\u4e3a\u53ef\u8bad\u7ec3\u53c2\u6570<\/strong>\uff0c\u5728\u5d4c\u5165\u7a7a\u95f4\u4e2d\u901a\u8fc7\u68af\u5ea6\u4e0b\u964d\u76f4\u63a5\u4f18\u5316\uff1a<\/p>\n

              \n
            • AutoPrompt<\/strong>\uff08Shin et al., 2020\uff09<\/li>\n
            • Prefix-Tuning<\/strong>\uff08Li & Liang, 2021\uff09<\/li>\n
            • P-tuning<\/strong>\uff08Liu et al., 2021\uff09<\/li>\n
            • Prompt-Tuning<\/strong>\uff08Lester et al., 2021\uff09<\/li>\n<\/ul>\n

              \u8d8b\u52bf\u662f\u4ece\u590d\u6742\u5230\u7b80\u6d01\u3002<\/p>\n

              APE\uff08\u81ea\u52a8\u63d0\u793a\u5de5\u7a0b\u5e08\uff09<\/strong>\uff08Zhou et al., 2022\uff09\u66f4\u8fdb\u4e00\u6b65\uff1a<\/p>\n

                \n
              1. \u8ba9 LLM \u6839\u636e\u5c11\u91cf\u793a\u4f8b\u751f\u6210\u5019\u9009\u6307\u4ee4<\/li>\n
              2. \u7528\u8bc4\u5206\u51fd\u6570\u7b5b\u9009\u6700\u4f18\u5019\u9009<\/li>\n
              3. \u901a\u8fc7\u8499\u7279\u5361\u6d1b\u641c\u7d22\u8fed\u4ee3\u4f18\u5316<\/li>\n<\/ol>\n

                \u81ea\u52a8\u6784\u5efa CoT \u63d0\u793a<\/strong>\uff08Shum et al., 2023\uff09\u91c7\u7528”\u589e\u5f3a-\u526a\u679d-\u9009\u62e9”\u4e09\u6b65\u6cd5\uff0c\u5148\u751f\u6210\u4f2a\u63a8\u7406\u94fe\uff0c\u518d\u6839\u636e\u6b63\u786e\u6027\u526a\u679d\uff0c\u6700\u540e\u7528\u7b56\u7565\u68af\u5ea6\u9009\u62e9\u6700\u4f18\u793a\u4f8b\u3002<\/p>\n

                \u3010\u9605\u8bfb\u603b\u7ed3\u3011\u63d0\u793a\u53ef\u4ee5\u81ea\u52a8\u5316\u4f18\u5316\uff0c\u4ece\u5d4c\u5165\u7a7a\u95f4\u68af\u5ea6\u5230 LLM \u81ea\u5df1\u751f\u6210\u6307\u4ee4\uff0c\u8d8b\u52bf\u662f\u8d8a\u6765\u8d8a\u81ea\u52a8\u5316\u3002<\/p>\n

                \n

                \u516d\u3001\u589e\u5f3a\u8bed\u8a00\u6a21\u578b\uff08Augmented Language Models\uff09<\/h2>\n

                6.1 \u68c0\u7d22\u589e\u5f3a\uff08Retrieval\uff09<\/h3>\n

                \u6a21\u578b\u8bad\u7ec3\u6570\u636e\u6709\u65f6\u6548\u6027\uff0c\u9047\u5230\u6700\u65b0\u4fe1\u606f\u5c31\u4f1a”\u5e7b\u89c9”\u3002\u68c0\u7d22\u589e\u5f3a\u751f\u6210\uff08RAG\uff09<\/strong>\u7684\u601d\u8def\u662f\uff1a\u5148\u4ece\u77e5\u8bc6\u5e93\u68c0\u7d22\u76f8\u5173\u5185\u5bb9\uff0c\u518d\u628a\u68c0\u7d22\u7ed3\u679c\u653e\u8fdb\u63d0\u793a\u4e2d\u3002<\/p>\n

                Lazaridou et al. (2022) \u7684\u65b9\u6cd5\uff1a\u7528 Google \u641c\u7d22\u83b7\u53d6\u6587\u6863 \u2192 \u6309 TF-IDF \u76f8\u4f3c\u5ea6\u6392\u5e8f\u6bb5\u843d \u2192 \u53d6\u6700\u76f8\u5173\u7684\u6bb5\u843d\u653e\u5165\u63d0\u793a\u3002<\/p>\n

                \u66f4\u6709\u8da3\u7684\u662f”\u5185\u90e8\u68c0\u7d22<\/strong>“\uff08Liu et al., 2022\uff09\u2014\u2014\u8ba9\u6a21\u578b\u5148\u751f\u6210\u5173\u4e8e\u67d0\u8bdd\u9898\u7684\u77e5\u8bc6\uff0c\u518d\u7528\u8fd9\u4e9b\u77e5\u8bc6\u56de\u7b54\u95ee\u9898\uff0c\u6548\u679c\u7adf\u7136\u4e5f\u5f88\u597d\u3002<\/p>\n

                6.2 \u7f16\u7a0b\u8bed\u8a00\u8f85\u52a9<\/h3>\n

                PAL<\/strong> \u548c PoT<\/strong> \u8ba9 LLM \u751f\u6210 Python \u4ee3\u7801\u6765\u89e3\u51b3\u6570\u5b66\u63a8\u7406\u95ee\u9898\uff0c\u628a\u8ba1\u7b97\u4ea4\u7ed9\u89e3\u91ca\u5668\uff0c\u6a21\u578b\u53ea\u8d1f\u8d23”\u601d\u8003”\uff1a<\/p>\n

                `<\/code>python<\/p>\n

                # PAL: \u8ba9\u6a21\u578b\u751f\u6210\u4ee3\u7801\u800c\u975e\u76f4\u63a5\u8ba1\u7b97<\/p>\n

                def solution():<\/p>\n

                tom_time = 30 * 4 # Elizabeth 30\u5206\u949f, Tom 4\u500d<\/p>\n

                return tom_time \/ 60 # \u8f6c\u6362\u4e3a\u5c0f\u65f6<\/p>\n

                `<\/code><\/p>\n

                6.3 \u5916\u90e8 API \u8c03\u7528<\/h3>\n

                Toolformer<\/strong>\uff08Schick et al., 2023\uff09\u8ba9\u6a21\u578b\u81ea\u4e3b\u5b66\u4e60<\/strong>\u4f55\u65f6\u8c03\u7528\u5de5\u5177\uff1a<\/p>\n

                  \n
                • \ud83d\udcd0 \u8ba1\u7b97\u5668\uff1a\u5f25\u8865\u7cbe\u786e\u8ba1\u7b97\u77ed\u677f<\/li>\n
                • \u2753 Q&A \u7cfb\u7edf\uff1a\u51cf\u5c11\u5e7b\u89c9<\/li>\n
                • \ud83d\udd0d \u641c\u7d22\u5f15\u64ce\uff1a\u83b7\u53d6\u6700\u65b0\u4fe1\u606f<\/li>\n
                • \ud83c\udf0d \u7ffb\u8bd1\u7cfb\u7edf\uff1a\u4f4e\u8d44\u6e90\u8bed\u8a00<\/li>\n
                • \ud83d\udcc5 \u65e5\u5386\uff1a\u611f\u77e5\u65f6\u95f4<\/li>\n<\/ul>\n

                  Toolformer \u7684\u8bad\u7ec3\u91c7\u7528\u81ea\u76d1\u7763\u65b9\u5f0f\uff1a\u8ba9\u6a21\u578b\u6807\u6ce8 API \u8c03\u7528\uff0c\u8fc7\u6ee4\u6389\u65e0\u7528\u7684\u8c03\u7528\uff08\u5373\u6ca1\u6709\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u9884\u6d4b\u540e\u7eed token \u7684\uff09\uff0c\u7136\u540e\u5fae\u8c03\u3002\u6574\u4e2a\u8fc7\u7a0b\u7c7b\u4f3c\u5f3a\u5316\u5b66\u4e60\u3002<\/p>\n

                  \n

                  \u76ee\u524d Toolformer \u4e0d\u652f\u6301\u94fe\u5f0f\u5de5\u5177\u8c03\u7528\uff08\u4e00\u4e2a\u5de5\u5177\u7684\u8f93\u51fa\u4f5c\u4e3a\u53e6\u4e00\u4e2a\u7684\u8f93\u5165\uff09\uff0c\u8fd9\u662f\u672a\u6765\u7684\u91cd\u8981\u65b9\u5411\u3002<\/p>\n<\/blockquote>\n

                  \u3010\u9605\u8bfb\u603b\u7ed3\u3011\u901a\u8fc7\u68c0\u7d22\u3001\u4ee3\u7801\u6267\u884c\u548c\u5916\u90e8 API\uff0cLLM \u53ef\u4ee5\u7a81\u7834\u81ea\u8eab\u77e5\u8bc6\u548c\u80fd\u529b\u7684\u8fb9\u754c\uff0cRAG \u548c Toolformer \u662f\u6700\u5177\u4ee3\u8868\u6027\u7684\u6280\u672f\u3002<\/p>\n

                  \n

                  \u672f\u8bed\u5bf9\u7167\u8868<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                  \u82f1\u6587\u672f\u8bed<\/th>\n\u4e2d\u6587\u8bd1\u6cd5<\/th>\n<\/tr>\n
                  Prompt Engineering<\/td>\n\u63d0\u793a\u5de5\u7a0b<\/td>\n<\/tr>\n
                  Zero-Shot \/ Few-Shot<\/td>\n\u96f6\u6837\u672c \/ \u5c11\u6837\u672c<\/td>\n<\/tr>\n
                  In-Context Learning<\/td>\n\u4e0a\u4e0b\u6587\u5b66\u4e60<\/td>\n<\/tr>\n
                  Chain-of-Thought (CoT)<\/td>\n\u601d\u7ef4\u94fe<\/td>\n<\/tr>\n
                  Self-Consistency<\/td>\n\u81ea\u4e00\u81f4\u6027\u91c7\u6837<\/td>\n<\/tr>\n
                  Tree of Thought (ToT)<\/td>\n\u601d\u60f3\u6811<\/td>\n<\/tr>\n
                  Soft Prompt<\/td>\n\u8f6f\u63d0\u793a<\/td>\n<\/tr>\n
                  Retrieval-Augmented Generation (RAG)<\/td>\n\u68c0\u7d22\u589e\u5f3a\u751f\u6210<\/td>\n<\/tr>\n
                  Alignment \/ Steerability<\/td>\n\u5bf9\u9f50 \/ \u53ef\u63a7\u751f\u6210<\/td>\n<\/tr>\n
                  Instruction Tuning<\/td>\n\u6307\u4ee4\u5fae\u8c03<\/td>\n<\/tr>\n
                  RLHF<\/td>\n\u57fa\u4e8e\u4eba\u7c7b\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60<\/td>\n<\/tr>\n
                  Toolformer<\/td>\n\u5de5\u5177\u589e\u5f3a\u8bed\u8a00\u6a21\u578b<\/td>\n<\/tr>\n<\/table>\n
                  \n

                  \u8bd1\u8005\u624b\u8bb0\uff1a5 \u4e2a\u5173\u952e\u6536\u83b7<\/h2>\n
                    \n
                  1. \u63d0\u793a\u5de5\u7a0b\u662f\u5b9e\u9a8c\u79d1\u5b66<\/strong>\uff1a\u6ca1\u6709\u4e07\u80fd\u516c\u5f0f\uff0c\u4e0d\u540c\u6a21\u578b\u3001\u4e0d\u540c\u4efb\u52a1\u9700\u8981\u4e0d\u540c\u7684\u63d0\u793a\u7b56\u7565\uff0c\u5927\u91cf\u5b9e\u9a8c\u662f\u5fc5\u7ecf\u4e4b\u8def\u3002<\/li>\n<\/ol>\n
                      \n
                    1. \u5c11\u6837\u672c\u7684\u5173\u952e\u5728\u4e8e\u8d28\u91cf\u548c\u6392\u5e8f<\/strong>\uff1a\u4e0d\u662f\u968f\u4fbf\u7ed9\u51e0\u4e2a\u4f8b\u5b50\u5c31\u884c\uff0c\u793a\u4f8b\u7684\u9009\u62e9\u3001\u591a\u6837\u6027\u548c\u6392\u5217\u987a\u5e8f\u90fd\u4f1a\u663e\u8457\u5f71\u54cd\u6548\u679c\uff0c\u9700\u8981\u6ce8\u610f\u5404\u79cd\u504f\u5dee\u3002<\/li>\n<\/ol>\n
                        \n
                      1. \u601d\u7ef4\u94fe\u662f\u8d28\u53d8\u800c\u975e\u91cf\u53d8<\/strong>\uff1a”Let’s think step by step” \u8fd9\u7b80\u5355\u4e00\u53e5\u8bdd\u5c31\u80fd\u5927\u5e45\u63d0\u5347\u63a8\u7406\u80fd\u529b\uff0c\u4f46 CoT \u5728\u5927\u6a21\u578b\u4e0a\u624d\u6709\u6548\uff0c\u5c0f\u6a21\u578b\u6536\u76ca\u6709\u9650\u3002<\/li>\n<\/ol>\n
                          \n
                        1. \u63d0\u793a\u53ef\u4ee5\u81ea\u52a8\u5316\u4f18\u5316<\/strong>\uff1a\u4ece\u624b\u52a8\u8bbe\u8ba1\u5230 APE \u81ea\u52a8\u641c\u7d22\uff0c\u518d\u5230\u68af\u5ea6\u4f18\u5316\u8f6f\u63d0\u793a\uff0c\u63d0\u793a\u5de5\u7a0b\u6b63\u5728\u4ece\u624b\u5de5\u827a\u8d70\u5411\u81ea\u52a8\u5316\u3002<\/li>\n<\/ol>\n
                            \n
                          1. \u5de5\u5177\u589e\u5f3a\u662f LLM \u7684\u4e0b\u4e00\u4e2a\u6218\u573a<\/strong>\uff1aRAG \u89e3\u51b3\u77e5\u8bc6\u65f6\u6548\u6027\u95ee\u9898\uff0cPAL \u628a\u8ba1\u7b97\u5916\u5305\u7ed9\u4ee3\u7801\uff0cToolformer \u8ba9\u6a21\u578b\u81ea\u5df1\u51b3\u5b9a\u4f55\u65f6\u8c03\u5de5\u5177\u2014\u2014LLM \u6b63\u5728\u4ece”\u80fd\u8bf4”\u8d70\u5411”\u80fd\u505a”\u3002<\/li>\n<\/ol>\n
                            \n

                            *\u4f5c\u8005\uff1a\u6500\u5ca9\u8005\uff0c\u6280\u672f\u603b\u76d1\uff0c19\u5e74 IT \u5168\u6808\u5b9e\u6218\u3002\u7cbe\u901a\u7f51\u7edc\u3001\u5b89\u5168\u3001\u4e91\u8ba1\u7b97\u3001\u5bb9\u5668\u3001\u6570\u636e\u5e93\u3001\u8d85\u7b97\uff0c\u6301\u8bc1 PMP\u3001ITIL\u3001CKA\u3001\u7f51\u7edc\u5de5\u7a0b\u5e08\u3001\u4fe1\u606f\u5b89\u5168\u7b49\u7ea7\u4fdd\u62a4\u3001AIX \u5929\u5de5\u8ba1\u5212\u3002\u4e3b\u5bfc\u8fc7\u591a\u4e2a\u5343\u4e07\u7ea7\u653f\u52a1\u4e0e\u667a\u6167\u57ce\u5e02\u9879\u76ee\uff0c\u4ece\u552e\u524d\u5230\u4ea4\u4ed8\u5168\u6d41\u7a0b\u6253\u901a\u3002\u70ed\u8877\u5f00\u6e90\uff0c\u65e5\u62f1\u4e00\u5352\uff0c\u6bcf\u5929\u5206\u4eab AI \u5b66\u4e60\u7b14\u8bb0\uff0c\u966a\u4f60\u4ece\u96f6\u57fa\u7840\u5230 AI \u8fbe\u4eba\u3002*<\/p>\n","protected":false},"excerpt":{"rendered":"

                            \u539f\u6587\u4f5c\u8005\uff1aLilian Weng | \u539f\u6587\u65e5\u671f\uff1a2023\u5e743\u670815\u65e5 | \u8bd1\u8005\uff1a\u6500\u5ca9\u8005 \u539f\u6587\u94fe\u63a5\uff1ahttps: […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[100],"tags":[],"class_list":["post-297","post","type-post","status-publish","format-standard","hentry","category-aixuexi"],"yoast_head":"\n\u63d0\u793a\u5de5\u7a0b\uff08Prompt Engineering\uff09 - \u6500\u5ca9\u8005<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/climbing.top\/index.php\/2026\/05\/26\/tishigongchengprompt-engineering\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u63d0\u793a\u5de5\u7a0b\uff08Prompt Engineering\uff09 - 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