Prompt Engineering 提示词工程的实践技巧（五）

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本篇博客主要总结了 DeepLearning AI 公开课程 Prompt Engineering 提示词工程 第六章节 Transferring 转换的内容，也可以理解为该章节的笔记。

在这个章节，我们将学习如何使用 LLM 帮助我们完成翻译、拼写和语法检查、语气调整、格式转化等文本任务。虽然现在已经有很多很强大的翻译软件，但是当应用到特定领域的时候，我们还是会需要根据自己的应用需求，构建特定的翻译应用。例如某个电影或者游戏文本的翻译，其中可能会涉及很多设定上的词语需要约定，如果直接使用通用的翻译软件，会得到四不像的翻译结果。

1. 翻译 Translation

LLM 大多是多语言训练的，因此能很好地胜任翻译工作。例如：

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prompt = f"""
Translate the following English text to Spanish: ```Hi, I would like to order a blender```
"""
## 输出
Hola, me gustaría ordenar una licuadora.

或者让 LLM 帮助识别语言：

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prompt = f"""
Tell me which language this is: ```Combien coûte le lampadaire?```
"""
## 输出
This is French.

或者，指示模型同时翻译成多种语言。在这里，示例中的提示词要求 LLM 翻译成法语、西班牙语和海盗英语。

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prompt = f"""
Translate the following  text to French and Spanish and English pirate: ```I want to order a basketball```
"""
## 输出
French: Je veux commander un ballon de basket
Spanish: Quiero ordenar un balón de baloncesto
English: I want to order a basketball

还可以指示 LLM 翻译成正式和非正式的西班牙语。

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prompt = f"""
Translate the following text to Spanish in both the formal and informal forms: 'Would you like to order a pillow?'
"""
## 输出
Formal: ¿Le gustaría ordenar una almohada?
Informal: ¿Te gustaría ordenar una almohada?

2. 通用翻译 Universal Translation

假设我们需要实现多种语言的相互翻译。那么，只需对上述的 prompt 稍加改进，加上一个循环操作，就可以实现。如下：

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user_messages = [
  "La performance du système est plus lente que d'habitude.",  # System performance is slower than normal         
  "Mi monitor tiene píxeles que no se iluminan.",              # My monitor has pixels that are not lighting
  "Il mio mouse non funziona",                                 # My mouse is not working
  "Mój klawisz Ctrl jest zepsuty",                             # My keyboard has a broken control key
  "我的屏幕在闪烁"                                               # My screen is flashing
] 

## Universal Translation
for issue in user_messages:
    prompt = f"Tell me what language this is in a single word: ```{issue}```"
    lang = get_completion(prompt)
    print(f"Original message ({lang}): {issue}")

    prompt = f"""
    Translate the following  text to English \
    and Korean: ```{issue}```
    """
    response = get_completion(prompt)
    print(response, "\n")
    
## 输出
Original message (French): La performance du système est plus lente que d'habitude.
English: "The system performance is slower than usual."

Korean: "시스템 성능이 평소보다 느립니다." 

Original message (Spanish): Mi monitor tiene píxeles que no se iluminan.
English: "My monitor has pixels that do not light up."

Korean: "내 모니터에는 빛나지 않는 픽셀이 있습니다." 

Original message (Italian): Il mio mouse non funziona
English: My mouse is not working
Korean: 내 마우스가 작동하지 않습니다 

Original message (Polish): Mój klawisz Ctrl jest zepsuty
English: My Ctrl key is broken
Korean: 제 Ctrl 키가 고장 났어요 

Original message (Chinese): 我的屏幕在闪烁
English: My screen is flickering
Korean: 내 화면이 깜박거립니다

结合之前章节所学的内容，我们还可以指示 LLM 以 JSON的格式返回，这种格式更容易在代码之间传递。

3. 语气和风格变换 Tone Transformation

LLM 可以使用不同的语气来输出内容，例如 DeepSeek 很擅长犀利的语气，豆包很擅长诙谐的语气。通常，我们写论文用到的语气会更学术化，写通知的语气会更官方，日常聊天的时候则会更随意一些，写小说的时候我们会希望语言华丽或朴素一些。我们可以在 prompt 中指示 LLM 以我们要求的语气进行翻译或者写作。

如下的例子中，我们指示 LLM 将给出的文本从俚语翻译成商业信件。可以看到，结果很好，不仅语气明显更正式了，连信件的格式也规范给出了。

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prompt = f"""
Translate the following from slang to a business letter: 
'Dude, This is Joe, check out this spec on this standing lamp.'
"""
## 输出
Dear Sir/Madam,

I am writing to bring to your attention the specifications of a standing lamp that I believe may be of interest to you. 

Sincerely,
Joe

4. 文本格式转换 Format Conversation

LLM 可以支持在不同的格式之间进行转换，例如 JSON，HTML，markdown，XML等。我们只需要描述输入和输出的格式，并给出需要转换的文本数据就可以。

在下方的例子中可以看到，我们给出了一个 JSON 格式的数据，LLM帮助我们转换成了一个 HTML 的 table。

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data_json = { "resturant employees" :[ 
    {"name":"Shyam", "email":"shyamjaiswal@gmail.com"},
    {"name":"Bob", "email":"bob32@gmail.com"},
    {"name":"Jai", "email":"jai87@gmail.com"}
]}

prompt = f"""
Translate the following python dictionary from JSON to an HTML table with column headers and title: {data_json}
"""
## 输出
<html>
<head>
    <title>Restaurant Employees</title>
</head>
<body>
    <table>
        <tr>
            <th>Name</th>
            <th>Email</th>
        </tr>
        <tr>
            <td>Shyam</td>
            <td>shyamjaiswal@gmail.com</td>
        </tr>
        <tr>
            <td>Bob</td>
            <td>bob32@gmail.com</td>
        </tr>
        <tr>
            <td>Jai</td>
            <td>jai87@gmail.com</td>
        </tr>
    </table>
</body>
</html>

在 jupyter notebook 可视化这段 HTML代码，可以看到如下图所示，非常工整。

html-table

5. 拼写/语法检查

拼写和语法检查是一个非常实用的功能，在需要使用非母语进行沟通时，可以借助 LLM 的帮助，对文本进行修正或润色。

第一个示例中，给出一些有语法或拼写错误的句子，循环遍历，要求 LLM 对文本进行校对并纠正。这里的关键词是 “Proofread and correct”。

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text = [ 
  "The girl with the black and white puppies have a ball.",  # The girl has a ball.
  "Yolanda has her notebook.", # ok
  "Its going to be a long day. Does the car need it’s oil changed?",  # Homonyms
  "Their goes my freedom. There going to bring they’re suitcases.",  # Homonyms
  "Your going to need you’re notebook.",  # Homonyms
  "That medicine effects my ability to sleep. Have you heard of the butterfly affect?", # Homonyms
  "This phrase is to cherck chatGPT for speling abilitty"  # spelling
]
for t in text:
    prompt = f"""Proofread and correct the following text and rewrite the corrected version. If you don't find
    and errors, just say "No errors found". Don't use any punctuation around the text:
    ```{t}```"""
    response = get_completion(prompt)
    print(response)
## 输出
'''
The girl with the black and white puppies has a ball.
No errors found
No errors found.
Their goes my freedom. There going to bring they’re suitcases.

No errors found.

Rewritten:
Their goes my freedom. There going to bring their suitcases.
You're going to need your notebook.
No errors found.
No errors found
'''

或者，直接输入一段长评，拜托 LLM 帮助校正。可以看到输出的内容句子结构完整了，也修正了一些拼写错误。

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text = f"""
Got this for my daughter for her birthday cuz she keeps taking mine from my room. Yes, adults also like pandas too. She takes it everywhere with her, and it's super soft and cute. One of the ears is a bit lower than the other, and I don't think that was designed to be asymmetrical. It's a bit small for what I paid for it though. I think there might be other options that are bigger for the same price. It arrived a day earlier than expected, so I got to play with it myself before I gave it to my daughter.
"""
prompt = f"proofread and correct this review: ```{text}```"
## 输出
'''
I got this for my daughter for her birthday because she keeps taking mine from my room. Yes, adults also like pandas too. She takes it everywhere with her, and it's super soft and cute. One of the ears is a bit lower than the other, and I don't think that was designed to be asymmetrical. It's a bit small for what I paid for it though. I think there might be other options that are bigger for the same price. It arrived a day earlier than expected, so I got to play with it myself before I gave it to my daughter.
'''

为了让前后的差异更直观，我们可以借助 python redlines 来对比前后文本的差异：

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from redlines import Redlines

diff = Redlines(text,response)
display(Markdown(diff.output_markdown))

redlines

我们还可以指示 LLM 在校对修正文本的基础上，改写语气，并要求符合 APA 风格（一种论文撰写风格），以markdown的格式返回。

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prompt = f"""
proofread and correct this review. Make it more compelling. 
Ensure it follows APA style guide and targets an advanced reader. 
Output in markdown format.
Text: ```{text}```
"""
display(Markdown(response))
## 输出
'''
I purchased this adorable panda plush as a birthday gift for my daughter, as she kept borrowing mine from my room. It's not just for kids - even adults can appreciate the charm of this cuddly companion. My daughter absolutely adores it and takes it everywhere with her, thanks to its irresistibly soft and cute design. However, I did notice a slight asymmetry in the ears, which I believe was not intentional. Additionally, considering the price, I found the size to be a bit smaller than expected. I suspect there are larger options available for the same cost. Despite this, I was pleasantly surprised when it arrived a day earlier than anticipated, allowing me to enjoy its company before passing it on to my daughter. This panda plush is a delightful gift choice, perfect for anyone who appreciates quality and cuteness in a stuffed animal.
'''

不过，虽然这里输出的语气已经比较正式，但是距离标准的APA风格还是有一段差距。比较好的返回效果应该是 Intro、Description、Props、Cons、Conclusion等分类分点阐述的。我重新运行了好几次返回都还是这样，不及视频里给出的示例预期。

Prompt Engineering 提示词工程笔记汇总

参考资料

ChatGPT Prompt Engineering for Developers - DeepLearning.AI

B站可以找到搬运翻译的中文版本👆：《吴恩达 x OpenAI Prompt课程》

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