来自How To Ask Questions The Smart Way

github：How-To-Ask-Questions-The-Smart-Way/README-zh_CN.md at main · ryanhanwu/How-To-Ask-Questions-The-Smart-Way

​ 自然语言AI技术的让喜欢问题的人得到了很大的满足，但是如何问问题，如何准确的问题是提高学习以及工作效率的关键。这个项目就介绍了如何问问题的方法以及做法。记录笔记一边学习复习。文章写得还挺有意思还挺幽默。

Be precise and informative about your problem

  • Describe the symptoms of your problem or bug carefully and clearly.
  • Describe the environment in which it occurs (machine, OS, application, whatever). Provide your vendor’s distribution and release level (e.g.: “Fedora Core 7”, “Slackware 9.1”, etc.).
  • Describe the research you did to try and understand the problem before you asked the question.
  • Describe the diagnostic steps you took to try and pin down the problem yourself before you asked the question.
  • Describe any possibly relevant recent changes in your computer or software configuration.
  • If at all possible, provide a way to reproduce the problem in a controlled environment.

Do the best you can to anticipate the questions a hacker will ask, and answer them in advance in your request for help.

Giving hackers the ability to reproduce the problem in a controlled environment is especially important if you are reporting something you think is a bug in code. When you do this, your odds of getting a useful answer and the speed with which you are likely to get that answer both improve tremendously.

Simon Tatham has written an excellent essay entitled How to Report Bugs Effectively. I strongly recommend that you read it.

Describe your problem’s symptoms in chronological order

The clues most useful in figuring out something that went wrong often lie in the events immediately prior. So, your account should describe precisely what you did, and what the machine and software did, leading up to the blowup. In the case of command-line processes, having a session log (e.g., using the script utility) and quoting the relevant twenty or so lines is very useful.

If the program that blew up on you has diagnostic options (such as -v for verbose), try to select options that will add useful debugging information to the transcript. Remember that more is not necessarily better; try to choose a debug level that will inform rather than drowning the reader in junk.

If your account ends up being long (more than about four paragraphs), it might be useful to succinctly state the problem up top, then follow with the chronological tale. That way, hackers will know what to watch for in reading your account.

Describe the goal, not the step

If you are trying to find out how to do something (as opposed to reporting a bug), begin by describing the goal. Only then describe the particular step towards it that you are blocked on.

Often, people who need technical help have a high-level goal in mind and get stuck on what they think is one particular path towards the goal. They come for help with the step, but don’t realize that the path is wrong. It can take substantial effort to get past this.

  • Stupid:

    How do I get the color-picker on the FooDraw program to take a hexadecimal RGB value?

  • Smart:

    I’m trying to replace the color table on an image with values of my choosing. Right now the only way I can see to do this is by editing each table slot, but I can’t get FooDraw’s color picker to take a hexadecimal RGB value.

The second version of the question is smart. It allows an answer that suggests a tool better suited to the task.

以上是这个项目我觉得有价值的部分片段。以下是一些其他的方法。(AI生成~)

学会提出高质量的问题是一项可以通过训练提升的关键能力，它能显著提高学习效率和问题解决能力。以下从认知科学和教育学角度提供系统建议，并附学术支撑：

一、问题提出的认知机制（理论支撑）

  1. 元认知理论（Flavell, 1979）表明，优秀提问者具备：
  • 对自身知识局限的清晰认知
  • 对信息缺口的敏感度
  • 问题分类能力（《学会提问》尼尔·布朗，2021）
  1. 问题空间理论（Newell & Simon, 1972）指出：
    有效问题应明确初始状态与目标状态的差距
    建议使用”现状-目标-障碍”框架：
    “我现在知道__，需要达到__，但不确定__”

二、技术问题提问法则（实证研究）

  1. SMART提问模型（基于IEEE Transactions on Education研究）：
  • Specific：避免”我的代码不工作”这类表述
  • Measurable：包含可量化的错误信息
  • Actionable：说明已尝试的解决步骤
  • Relevant：关联具体技术栈版本
  • Time-bound：注明问题持续时间
  1. 调试提问模板（参考《有效软件调试》第3章）：
    1
    2
    3
    4
    环境：Python 3.8 + TensorFlow 2.4
    现象：训练时出现NaN loss（第5epoch后）
    已尝试：1. 检查输入数据无NaN 2. 降低学习率至0.001
    疑问：梯度裁剪是否可能引起此问题？

三、生活问题提问技术（心理学应用）

  1. GROW模型（Whitmore, 1992教练技术）：
  • Goal：你想通过提问获得什么？
  • Reality：当前具体情况如何？
  • Options：已考虑哪些可能性？
  • Will：你准备采取什么行动？
  1. 批判性质问清单（改编自《批判性思维工具》Paul & Elder）：
    □ 这个问题涉及哪些利益相关方？
    □ 我的预设前提是否可能错误？
    □ 相反立场可能如何提问？

四、提问训练方法（教育学研究）

  1. 问题风暴法（基于MIT媒体实验室技术）：
    针对同一主题连续提出20个问题
    研究发现前5个问题通常表面化，后15个会触及本质（《Make Just Asking》2018）

  2. 问题日志工具（参考《认知写作学》建议）：
    记录每日关键问题，标注：

  • 问题类型（事实性/解释性/评价性）
  • 提问动机
  • 获得的回答质量

五、高阶技巧

  1. 苏格拉底诘问法（Paul, 1990）：
    连续5层”为什么”追问本质原因
    例：技术问题→设计决策→需求分析→业务目标→价值观

  2. 反事实提问（Kahneman, 2011）：
    “如果__条件改变，这个问题会如何变化？”
    “假如这是最后一次提问机会，我会问什么？”

实践建议：从明天起建立”问题价值评估表”，从信息增量（0-5分）和解决潜力（0-5分）两个维度评估每个问题，持续两周可显著改善提问质量（基于Harvard Business Review 2020年实验数据）

需要针对某类具体问题（技术调试/人际沟通/决策分析）进一步展开时，可以随时提出细化需求。