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  • [正版新书]零基础掌握学术提示工程 宋义平、郭泽德、赵鑫 清华大学出版社 人工智能-应用-学术研究
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    • 作者: 宋义平、郭泽德、赵鑫著
    • 出版社: 清华大学出版社
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    • 作者: 宋义平、郭泽德、赵鑫著
    • 出版社:清华大学出版社
    • 开本:32开
    • ISBN:9788059122010
    • 版权提供:清华大学出版社

     书名:  零基础掌握学术提示工程
     出版社:  清华大学出版社
     出版日期  2023
     ISBN号:  9787302644415
    本书聚焦于解决学术群体如何使用恰当的提示词与人工智能进行高效互动,进而提升学术效率,实现学术工具的革新。本书在讲解提示词的同时,将结合具体的学术场景,例如选题确定、概念画图、文献整理、质性研究编码、表格制作等,给出具体的提问示例及讲解。通过手把手的方式带领用户掌握学术提示工程,从而在学术界不断保持竞争力。

    宋义平,北京思高乐教育科技有限公司总经理,中国人民大学社会学博士,百万粉丝学术教育平台“学术志”联合创始人、金牌讲师,策划出版大量知名教材与在线课程。

    这本书适合想借助新技术提升学术研究效率的人员:

    一、高度重视实操,提供完整提示词及输出示例

    二、中英文交替提问,回答更精确

    三、预设提问模板,新手可直接复制使用

    四、少踩坑,规避错误提问方式

    五、文末提供练习,启发读者认知升级


    第一章 提示工程与学术研究效率

    引子:鲁提辖与ChatGPT·············································2

    第一节  什么是prompt模型?······································4

    第二节 高质量提问的整体学习框架······························11

    第三节  本书的写作逻辑············································15


    第二章 常见的错误提问方式

    第一节  就事论事型··················································20

    第二节  画地为牢型··················································24

    第三节  想当然型·····················································29

    第四节  简单粗暴型··················································35

    第五节  问题不当型··················································43

    第六节  缺乏比较型··················································45


    第三章 prompt初级模型(上)

    第一节  指示模型·····················································55

    第二节  角色设定模型···············································65

    第三节  简单任务模型···············································72

    第四节  案例示范模型···············································82

    第五节  头脑风暴模型···············································85


    第四章 prompt初级模型(下)

    第一节  逻辑自洽模型···············································92

    第二节  关键词模型················································102

    第三节  知识生产模型············································· 110

    第四节  知识整合模型·············································121

    第五节  决策模型···················································128


    第五章 prompt进阶模型(上)

    第一节  弹性指示模型·············································140

    第二节  明确指示模型·············································162

    第三节  知识扩展模型·············································172

    第四节  摘要提炼模型·············································179

    第五节  强化学习模型·············································184


    第六章 prompt进阶模型(下)

    第一节  情感分析模型·············································202

    第二节  实体区分模型·············································208

    第三节  文本分类模型·············································224

    第四节  横纵比较模型·············································230

    第五节  对外拓展模型·············································243


    第七章 使用框架模型解决复杂学术问题

    第一节  什么是框架模型?·······································268

    第二节  作为通用形式模型的SRGCD模型····················269

    第三节  解决实质性问题的内容模型····························272

    第四节 一个实例:如何用ChatGPT形成高质量选题·······277


    结语 一段未竟的进化之旅



     

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