Prompt learning 教学[基础篇]:prompt基本原则以及使用场景技巧助力你更好使用chatgpt,得到你想要的答案
- 如果你想系统学习
- 如果你对 AI 和 Prompt Engineering 不是很了解,甚至连 ChatGPT 也不是很了解,那我建议你从基础篇开始读起。基础篇更多的会从用户的角度教你如何使用 AI 产品,或者换句话说,会讲更多 prompt 的内容。
- 如果你已经了解基本的用法,并且想要学习如何更好地开发 AI 产品,想了解更多 prompt engineering 的内容。那可以直接跳去高级篇阅读。
- 如果你只是想快速学会使用
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如果你只是想快速了解使用 AI 产品的技巧,你可以直接读技巧篇,那里汇总了所有使用技巧。
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如果你已经了解如何使用了,但想了解更多使用场景,可以看看资料 & 产品推荐篇。
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0.Dyno Prompt Engineering介绍
Dyno Prompt Engineering IDE是一款基于人工智能技术的集成开发环境(IDE),它可以帮助开发者更快速、更高效地进行软件开发。Dyno Prompt Engineering IDE的主要特点包括以下几个方面:
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智能代码提示:Dyno Prompt Engineering IDE可以通过分析代码语法和上下文,智能地提供代码提示和自动补全功能,帮助开发者更快速地编写代码。
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智能错误检测:Dyno Prompt Engineering IDE可以通过分析代码语法和逻辑,智能地检测代码中的错误和潜在问题,并提供相应的修复建议,帮助开发者更快速地调试代码。
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智能重构:Dyno Prompt Engineering IDE可以通过分析代码结构和依赖关系,智能地进行代码重构和优化,帮助开发者更高效地改进代码质量和性能。
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智能集成:Dyno Prompt Engineering IDE可以与其他开发工具和平台进行智能集成,例如版本控制系统、测试工具、部署平台等,帮助开发者更高效地进行软件开发和管理。
Dyno Prompt Engineering IDE是一款基于人工智能技术的集成开发环境,它可以帮助开发者更快速、更高效地进行软件开发,提高开发效率和代码质量。
1.Prompt Engineering简介
解释这个词之前,首先需要解释 prompt 这个词。简单的理解它是给 AI 模型的指令。可以是一个问题、一段文字描述,甚至可以是带有一堆参数的文字描述。AI 模型会基于 prompt 所提供的信息,生成对应的文本,亦或者图片。比如,我们在 ChatGPT 里输入 What is the capital of China?
(中国的首都是什么?),这个问题就是 prompt。
而 Prompt Engineering (中文意思为提示工程,后缩写为 PE)则是:
Prompt Engineering 是一种人工智能(AI)技术,它通过设计和改进 AI 的 prompt 来提高 AI 的表现。Prompt Engineering 的目标是创建高度有效和可控的 AI 系统,使其能够准确、可靠地执行特定任务。
看上去很难懂,我试着换个方式让你理解。你可能用过不少 AI 相关的产品,你或许会觉得好像只需要会说话、会打字,就能让 AI 输出答案。好像不需要什么技术。的确,如果你只想让 AI 给你答案,你不需要额外做什么,只需要输入文字即可。但如果你想要得到满意的答案,甚至精确的答案。就需要用到 PE 这个技术。因为人类的语言从根本上说是不精确的,目前机器还没法很好地理解人类说的话,所以才会出现 PE 这个技术。另外,受制于目前大语言模型 AI 的实现原理,部分逻辑运算问题,需要额外对 AI 进行提示(这里你不需要深究原因,暂时先知道这是个问题即可)。
举个例子,如果我们在 ChatGPT 里输入这样的一段话:
What is 100*100/400*56?
ChatGPT 会返回一个错误的答案 0.4464(注意,如果你用下方的 Dyno 运行,答案应该也是错误的,API 版本的回答是 14):但如果我们对 prompt 进行一些修改,答案则会是正确的。注意,如果你用下方的 Dyno 运行旧版的模型 API 的版本,做了修改后,答案应该还是错误的,需要用到 Role Prompting 才能生成正确答案。(这个后续章节会讲解)另外,目前的 AI 产品还比较早期,因为各种原因,产品设置了很多限制,如果你想要绕过一些限制,或者更好地发挥 AI 的能力,也需要用到 Prompt Engineering 技术。这个我们在后续的章节会讲到。
所以,总的来说,Prompt Engineering 是一种重要的 AI 技术:
- 如果你是 AI 产品用户,可以通过这个技术,充分发挥 AI 产品的能力,获得更好的体验,从而提高工作效率。
- 如果你是产品设计师,或者研发人员,你可以通过它来设计和改进 AI 系统的提示,从而提高 AI 系统的性能和准确性,为用户带来更好的 AI 体验。
2.prompt基本原则
在和 ChatGPT 对话时,亦或者在使用和设计 prompt 时,有以下几个原则与建议。记住这几个原则,能让你写出更好的 prompt。如果你是直接使用 AI 产品,比如 ChatGPT 或者 Midjourney,那无需在意这个原则。如果你是通过 API 或者 OpenAI Playground 的方式使用,则建议你先使用最新的模型测试。
2.1 Prompt 里最好包含完整的信息
这个是对结果影响最大的因素。比如如果你想让 AI 写一首关于 OpenAI 的诗。
Less effective prompt:
Write a poem about OpenAI.
它生成的答案可能就会很宽泛,而更好的方式是增加更多的信息。
Better prompt:
Write a short inspiring poem about OpenAI, focusing on the recent DALL-E product launch (DALL-E is a text to image ML model) in the style of a {famous poet}
2.2 Prompt 最好简洁易懂,并减少歧义
这个比较好理解,即使你跟人说话,说一些简单的短句,对方也会更容易听懂,AI 也是这样。另外,在 prompt 里也需要减少一些歧义,少用模棱两可的词语。
比如像这个就很不明确,什么叫 not too much more?
The description for this product should be fairly short, a few sentences only, and not too much more.
更好的 prompt 是这样的,明确告知要写多少句话,就比较明确:
Use a 3 to 5 sentence paragraph to describe this product.
另外需要注意的是,简单并不代表简短。你的 prompt 也可以很长,只要你的 prompt 描述更充分就可以,即使长一点也没有关系。
2.3 Prompt 要使用正确的语法、拼写,以及标点,从简单的先开始,并给产品多一点耐心
最后一点算是我个人的建议。如我在前面提到的例子 What is 100*100/40*56?
一样,如果发现机器理解错误,不妨补充点信息,不妨多实验几次,给 AI 多一点耐心。
3.基本使用场景和使用技巧
3.1场景1:问答问题
这个场景应该是使用 AI 产品最常见的方法。以 ChatGPT 为例,一般就是你提一个问题,ChatGPT 会给你答案,比如像这样:
在这个场景下,prompt 只要满足前面提到的基本原则,基本上就没有什么问题。但需要注意,不同的 AI 模型擅长的东西都不太一样,prompt 可能需要针对该模型进行微调。另外,目前的 AI 产品,也不是无所不能,有些问题你再怎么优化 prompt 它也没法回答你。以 ChatGPT 为例:
- ChatGPT 比较擅长回答基本事实的问题,比如问
什么是牛顿第三定律?
。但不太擅长回答意见类的问题,比如问它谁是世界第一足球运动员?
,它就没法回答了。 - 另外,ChatGPT 的数据仅有 2021 年 9 月以前的,如果你问这个时间以后的问题,比如
现在的美国总统是谁?
它的答案是「截至2021年9月,现任美国总统是乔·拜登(Joe Biden)。」
另外,正如我在前面基础用法一章中提到的那样,问答场景里还有一个很重要的玩法,就是多轮聊天,你可以针对某个问题,进行多轮的提问。
使用技巧一:To do and Not To do
我介绍的技巧其实在各个场景都可以使用,我将其放在某个场景下解释,只是因为我觉得它更有可能在这个场景用到。你也会更容易记住这个用法。并不意味着这个技巧仅能在此场景使用。并且多技巧混用也是个不错的用法。
在问答场景里,为了让 AI 回答更加准确,一般会在问题里加条件。比如让 AI 推荐一部电影给你 Recommend a movie to me
。但这个 prompt 太空泛了,AI 无法直接回答,接着它会问你想要什么类型的电影,但这样你就需要跟 AI 聊很多轮,效率比较低。
所以,为了提高效率,一般会在 prompt 里看到类似这样的话(意思是不要询问我对什么感兴趣,或者问我的个人信息):
DO NOT ASK FOR INTERESTS. DO NOT ASK FOR PERSONAL INFORMATION.
如果你在 ChatGPT 里这样提问,或者使用 ChatGPT 最新的 API ,它就不会问你问题,而是直接推荐一部电影给你,它的 Output 是这样的:
Certainly! If you're in the mood for an action-packed movie, you might enjoy "John Wick" (2014), directed by Chad Stahelski and starring Keanu Reeves. The movie follows a retired hitman named John Wick who seeks vengeance against the people who wronged him. It's a fast-paced and stylish film with lots of thrilling action sequences and an engaging story. If you're looking for something that will keep you on the edge of your seat, "John Wick" is definitely worth a watch!
但如果你使用的是如 Davinci-003 这样的模型,它的 Output 很可能是这样的,它还会问你的兴趣爱好:
Sure, I can recommend a movie based on your interests. What kind of movie would you like to watch? Do you prefer action, comedy, romance, or something else?
所以 OpenAI 的 API 最佳实践文档里,提到了一个这样的最佳实践:
Instead of just saying what not to do, say what to do instead. 与其告知模型不能干什么,不妨告诉模型能干什么。
我自己的实践是,虽然现在最新的模型已经理解什么是 Not Todo ,但如果你想要的是明确的答案,加入更多限定词,告知模型能干什么,回答的效率会更高,且预期会更明确。还是电影推荐这个案例,你可以加入一个限定词:
Recommend a movie from the top global trending movies to me.
当然并不是 Not Todo 就不能用,如果:
- 你已经告知模型很明确的点,然后你想缩小范围,那增加一些 Not Todo 会提高不少效率。
- 你是在做一些探索,比如你不知道如何做精准限定,你只知道不要什么。那可以先加入 Not Todo ,让 AI 先发散给你答案,当探索完成后,再去优化 prompt。
以下是一些场景案例,我整理了两个 Less Effective(不太有效的) 和 Better(更好的) prompt,你可以自己尝试下这些案例:
场景 | Less Effective | Better | 原因 |
---|---|---|---|
推荐雅思必背英文单词 | Please suggest me some essential words for IELTS | Please suggest me 10 essential words for IELTS | 后者 prompt 会更加明确,前者会给大概 20 个单词。这个仍然有提升的空间,比如增加更多的限定词语,像字母 A 开头的词语。 |
推荐香港值得游玩的地方 | Please recommend me some places to visit in Hong Kong. Do not recommend museums. | Please recommend me some places to visit in Hong Kong including amusement parks. | 后者的推荐会更准确高效一些,但如果你想进行一些探索,那前者也能用。 |
3.2 基于实例回答
在某些场景下,我们能比较简单地向 AI 描述出什么能做,什么不能做。但有些场景,有些需求很难通过文字指令传递给 AI,即使描述出来了,AI 也不能很好地理解。
比如给宠物起英文名,里面会夹杂着一些所谓的名字风格。此时你就可以在 prompt 里增加一些例子,我们看看这个例子。
这个是没有任何示例的 Prompt:
Suggest three names for a horse that is a superhero.
Output 如下所示。第一个感觉还行,第二个 Captain 有 hero 的感觉,但 Canter 就像是说这匹马跑得很慢,感觉不太合适,而且三个都比较一般,不够酷。
Thunder Hooves, Captain Canter, Mighty Gallop
技巧 2:增加示例
如果你无法用文字准确解释问题或指示,你可以在 prompt 里增加一些案例:
Suggest three names for an animal that is a superhero.
Animal: Cat
Names: Captain Sharpclaw, Agent Fluffball, The Incredible Feline
Animal: Dog
Names: Ruff the Protector, Wonder Canine, Sir Barks-a-Lot
Animal: Horse
Names:
增加例子后,Output 的结果就更酷一些,或者说是接近我想要的那种风格的名字。
Gallop Guardian, Equine Avenger, The Mighty Stallion
以下是一些场景案例,我整理了两个 Less Effective(不太有效的)和 Better(更好的)prompt,你可以自己尝试下这些案例:
场景 | Less Effective | Better | 原因 |
---|---|---|---|
起产品名 | Product description: A pair of shoes that can fit any foot size. Seed words: adaptable, fit, omni-fit. Product names: |
Product description: A home milkshake maker Seed words: fast, healthy, compact. Product names: HomeShaker, Fit Shaker, QuickShake, Shake Maker Product description: A pair of shoes that can fit any foot size. Seed words: adaptable, fit, omni-fit. Product names: |
可以在下方运行这个案例,在不给示例的情况下 AI 会给你什么答案。 |
将电影名称转为 emoji | Convert Star Wars into emoji | Convert movie titles into emoji. Back to the Future: 👨👴🚗🕒 Batman: 🤵🦇 Transformers: 🚗🤖 Star Wars: |
可以在下方运行这个案例,在不给示例的情况下 AI 会给你什么答案。 |
3.3 推理
在问答这个大场景下,还有一个子场景是推理,这个场景非常有意思,而且是个非常值得深挖的场景,prompt 在此场景里发挥的作用非常大。
如果你想用 ChatGPT API 做点什么小应用,我建议可以从这个场景入手,相对来说没有其他场景那么红海。
举个比较简单的例子,我将美国工商管理硕士的入学考试 GMAT 的一道数学题输入进了 ChatGPT:
If a real estate agent received a commission of 6 percent of the selling price of a certain house, what was the selling price of the house?
(1) The selling price minus the real estate agent’s commission was $84,600.
(2) The selling price was 250 percent of the original purchase price of $36,000.
(A) Statement (1) ALONE is sufficient, but statement (2) alone is not sufficient.
(B) Statement (2) ALONE is sufficient, but statement (1) alone is not sufficient.
(C) BOTH statements TOGETHER are sufficient, but NEITHER statement ALONE is sufficient.
(D) EACH statement ALONE is sufficient.
(E) Statements (1) and (2) TOGETHER are NOT sufficient.
这道题目的中文意思是这样的(你不妨也思考下这个题目?):
如果一个房地产经纪人的佣金是某个房子的售价的6%,那么这个房子的售价是多少?
(1)售价减去房地产经纪人的佣金为84,600美元。
(2)购买价是36,000美元,售价是购买价的250%。
(A)仅陈述(1)足以回答问题,但仅陈述(2)不能回答问题。
(B)仅陈述(2)足以回答问题,但仅陈述(1)不能回答问题。
(C)两个陈述合起来足以回答问题,但没有一个陈述单独足以回答问题。
(D)每个陈述单独足以回答问题。
(E)陈述(1)和(2)合起来不能回答问题。
它的 Output 是这样的,它会根据题目的要求去推理和运算:
Let's assume the selling price of the house is S.
According to the given information, the real estate agent's commission is 6% of S, which is 0.06S.
So, the amount received by the seller after paying the commission would be (S - 0.06S) = 0.94S.
Statement (1) tells us that 0.94S = $84,600. Solving for S, we get S = $90,000.
Therefore, statement (1) alone is sufficient to determine the selling price of the house.
Statement (2) tells us that the selling price is 250% of the original purchase price, which means S = 2.5*$36,000 = $90,000.
Therefore, statement (2) alone is also sufficient to determine the selling price of the house.
Since both statements individually provide the same answer, we can conclude that together they are also sufficient to determine the selling price of the house.
Hence, the answer is (D) EACH statement ALONE is sufficient.
翻译成中文是这样的:
假设房子的售价为S。
根据所给信息,房地产经纪人的佣金是S的6%,即0.06S。
因此,在支付佣金后卖方收到的金额为(S-0.06S)= 0.94S。
陈述(1)告诉我们,0.94S = 84,600美元。解出S,我们得到S = $90,000。
因此,陈述(1)单独足以确定房子的售价。
陈述(2)告诉我们,售价是原始购买价的250%,这意味着S = 2.5 * $36,000 = $90,000。
因此,陈述(2)单独也足以确定房子的售价。
由于两个陈述分别提供相同的答案,我们可以得出结论,它们共同足以确定房子的售价。
因此,答案是(D)每个陈述单独足以回答问题。
这个场景还比较简单,我就不在这举更多例子了,你可以将各种题目输入进 ChatGPT 里试试。
3.4 写代码
除了回答问题外,另一个重要的场景是让 AI 根据你的要求完成一些内容生成任务,根据输出的结果差异,我将其概括为以下几个主要场景:
- 无中生有
- 锦上添花
- 化繁为简
本章,我们先来聊聊「无中生有」场景。顾名思义,就是让 AI 给你生成一些内容。你只需要向 AI 描述你想写的内容,AI 就会按你的要求写出该内容。比如:
- 撰写招聘信息
- 撰写电商平台的货物描述
- 撰写短视频脚本
- 甚至让它写代码都可以
像撰写招聘信息等,你只需要明确目标,将目标定得比较明确即可生成能让你较为满意的答案。本章我想聊下写代码。下面是一个让 ChatGPT 写代码的案例。
Prompt:
Create a MySQL query for all students in the Computer Science Department:
Table departments, columns = [DepartmentId, DepartmentName]
Table students, columns = [DepartmentId, StudentId, StudentName]
Output:
SELECT students.*
FROM students
INNER JOIN departments
ON students.DepartmentId = departments.DepartmentId
WHERE departments.DepartmentName = 'Computer Science';
技巧 3:使用引导词,引导模型输出特定语内容
在代码生成场景里,有一个小技巧,上面提到的案例,其 prompt 还可以继续优化,在 prompt 最后,增加一个代码的引导,告知 AI 我已经将条件描述完了,你可以写代码了。
在 prompt 的最后增加 SELECT 可以很好地提示 AI 可以写 SQL 代码了。Better prompt:
Create a MySQL query for all students in the Computer Science Department:
Table departments, columns = [DepartmentId, DepartmentName]
Table students, columns = [DepartmentId, StudentId, StudentName]
SELECT
同样的道理,如果你想让 AI 写 Python 代码,那 import 会是比较好的提示。但需要注意,这个只是告知 AI 可以写代码了,并不能告知 AI 写何种代码,你仍然需要在 prompt 里增加提示,告知 AI 要生成何种语言的代码。
在吴恩达的 ChatGPT Prompt Engineering 课程中,也提到这个技巧,只是在课程中,引导词并不是放在最后,而是在 prompt 里直接说明,让 AI 生成一个 JSON 格式的内容。课程中的例子是这样的(注意这个是 python 代码):
prompt = f"""
Generate a list of three made-up book titles along
with their authors and genres.
Provide them in JSON format with the following keys:
book_id, title, author, genre.
"""
3.5 修改内容(润色、翻译、查错)
除了从 0 生成内容外,你也可以先给 AI 一段已经写好的内容,然后让 AI 对其进行修改,让 AI 帮你将内容改得更好。实际的场景有如:
- 翻译:将中文文档翻译成英文,亦或者将英文翻译成中文。关于翻译,我还想强调,像 ChatGPT 除了能翻译人类的语言外,还能翻译编程语言,比如将 Python 代码翻译成 Haskell 代码。
- 修改:修改内容的语法,甄别内容里的错别字。
- 润色:润色文章,将内容改成另一种风格。
本章重点介绍下润色场景,下面这是个润色的例子。
Prompt(内容来自维基百科):
Please rewrite the following sentences to make them easier to understand.
OpenAI is an American artificial intelligence (AI) research laboratory consisting of the non-profit OpenAI Incorporated (OpenAI Inc.) and its for-profit subsidiary corporation OpenAI Limited Partnership (OpenAI LP). OpenAI conducts AI research with the declared intention of promoting and developing a friendly AI. OpenAI systems run on the fifth most powerful supercomputer in the world.[5][6][7] The organization was founded in San Francisco in 2015 by Sam Altman, Reid Hoffman, Jessica Livingston, Elon Musk, Ilya Sutskever, Peter Thiel and others,[8][1][9] who collectively pledged US$1 billion. Musk resigned from the board in 2018 but remained a donor. Microsoft provided OpenAI LP with a $1 billion investment in 2019 and a second multi-year investment in January 2023, reported to be $10 billion.[10]
它的 Output 是这样的:
OpenAI is a research organization that focuses on artificial intelligence (AI). It has two parts: a non-profit called OpenAI Incorporated and a for-profit subsidiary called OpenAI Limited Partnership. The organization's goal is to create AI that is friendly and beneficial to humans. OpenAI uses one of the most powerful supercomputers in the world for its research. The organization was founded in San Francisco in 2015 by a group of people who pledged a total of $1 billion, including Sam Altman, Reid Hoffman, Jessica Livingston, Elon Musk, Ilya Sutskever, and Peter Thiel. Musk resigned from the board in 2018 but still supports the organization financially. In 2019, Microsoft invested $1 billion in OpenAI LP, and in January 2023, they made a second investment reported to be $10 billion.
技巧 4:增加 Role(角色)或人物
前面提到的改写例子,我在 prompt 里加了 Role 让其更易于阅读,这个优化方法是 OK 的。也很常用,比如你想将你写的 Email 改得更商务,则只需要加 business 相关的词即可。
我再介绍一个更有效的技巧,就是在 prompt 里增加一些 role(角色)相关的内容,让 AI 生成的内容更符合你的需求。
比如还是上面那个 rewrite 的例子,我在例子前加入这样的一段话,我让 AI 假设自己是一个小学老师,并且很擅长将复杂的内容转变成 7、8 岁小朋友也能听懂的话,然后再改写这段话:
You are a primary school teacher who can explain complex content to a level that a 7 or 8 year old child can understand. Please rewrite the following sentences to make them easier to understand:
Output 就变成了下面这样。不管是用词,还是语法,看起来都更容易懂了。
OpenAI is a place where people work on making computers really smart. It's like a lab where they do research to make computers do amazing things! They want the computers to be really friendly and helpful to people. They use one of the most powerful computers in the world to do this research. A bunch of really rich and smart people gave money to start this lab. One of them is Elon Musk, but he left in 2018. Another really big company, Microsoft, gave them a lot of money too.
另外除了增加角色外,还能让 AI 模拟某个人,比如让 AI 以 David Foster Wallace 的风格改写上述那段话:
Imagine you are the famous writer David Foster Wallace, please rewrite this sentences.
Output 是这样的:
As David Foster Wallace, I might rewrite these sentences to sound more like this:
OpenAI is a lab that studies something called artificial intelligence, which is like when computers can do things that normally only humans can do. They have a big computer that's one of the most powerful in the world, and they're trying to make sure the AI they create is friendly and helpful. The lab was started in 2015 in a city called San Francisco, and a bunch of important people gave them a lot of money to do their research. One of those people was Elon Musk, who stopped working there but still gives them money, and Microsoft also gave them a lot of money recently.
关于这个技巧的应用,以及如何更好地使用 AI 提升写作能力,建议各位阅读 Nat Eliason 的 Using ChatGPT as a Writing Coach,他使用 ChatGPT 辅助其写作,就用到了上述的技巧。
3.6 内容解释(代码注释、论文解释等)
锦上添花下的第二大的场景是信息解释。它跟改写内容有点像,但又不太一样,信息解释有可能提供超过原文内容的信息。
举几个信息解释的例子,大家应该就能理解了:
- 解释代码:比如你看到一段 Python 的代码,但你看不懂,你可以让 AI 解释下代码的含义。
- 解释论文:看某篇论文看不懂,或者论文里的某一段看不懂,你也可以让 AI 解释。
以解释代码为例,它的 prompt 是这样的(此案例来自 OpenAI 官方案例):
Explanation of what the code does:
Python 3
def remove_common_prefix(x, prefix, ws_prefix):
x["completion"] = x["completion"].str[len(prefix) :]
if ws_prefix:
# keep the single whitespace as prefix
x["completion"] = " " + x["completion"]
return x
以下是 ChatGPT 的 Output,它阐述了这段代码是拿来干什么的,同时还阐述了各个参数的含义:
This is a Python function called remove_common_prefix that takes in three parameters: x, prefix, and ws_prefix.
The x parameter is assumed to be a Pandas DataFrame or Series object with a column named "completion", which contains strings that may or may not start with the prefix string.
The prefix parameter is a string that represents the common prefix that needs to be removed from the beginning of each string in the "completion" column of the DataFrame.
The ws_prefix parameter is a Boolean value that indicates whether or not to keep a single whitespace character as a prefix after the common prefix has been removed.
The function modifies the "completion" column of the DataFrame by removing the prefix string from the beginning of each string in that column using the Pandas str method and string slicing. The len(prefix) : syntax is used to slice the string starting from the character that follows the prefix.
If ws_prefix is True, then the function adds a single whitespace character as a prefix to each string in the "completion" column after removing the prefix.
Finally, the modified DataFrame is returned by the function.
3.7 信息总结(摘要)
内容生成大场景下的第二个场景是化繁为简,这个场景其实很好理解,就是将复杂的内容,转为简单的内容,一般常遇到的场景有:
- 信息总结:顾名思义,就是对一堆信息进行总结。
- 信息解释:这个跟改写内容有点像,但这个更偏向于解释与总结。下一章会给大家介绍更多的例子。
- 信息提取:提取信息里的某一段内容,比如从一大段文字中,找到关键内容,并分类。
本章会讲一下信息总结。信息总结还是比较简单的,基本上在 prompt 里加入总结 summarize 就可以了。但如果你想要一些特别的效果,不妨组合使用之前介绍的技巧,比如:
- 使用技巧 2,增加总结示例,让 AI 总结符合你需求的内容
- 使用技巧 4,增加 role,让 AI 总结的内容具有一定的风格
不过在这个场景,还有个技巧需要各位注意。
技巧 5:使用特殊符号将指令和需要处理的文本分开
不管是信息总结,还是信息提取,你一定会输入大段文字,甚至多段文字,此时有个小技巧。
可以用“”“将指令和文本分开。根据我的测试,如果你的文本有多段,增加”“”会提升 AI 反馈的准确性(这个技巧来自于 OpenAI 的 API 最佳实践文档)
像我们之前写的 prompt 就属于 Less effective prompt。为什么呢?据我的测试,主要还是 AI 不知道什么是指令,什么是待处理的内容,用符号分隔开来会更利于 AI 区分。
Please summarize the following sentences to make them easier to understand.
OpenAI is an American artificial intelligence (AI) research laboratory consisting of the non-profit OpenAI Incorporated (OpenAI Inc.) and its for-profit subsidiary corporation OpenAI Limited Partnership (OpenAI LP). OpenAI conducts AI research with the declared intention of promoting and developing a friendly AI. OpenAI systems run on the fifth most powerful supercomputer in the world.[5][6][7] The organization was founded in San Francisco in 2015 by Sam Altman, Reid Hoffman, Jessica Livingston, Elon Musk, Ilya Sutskever, Peter Thiel and others,[8][1][9] who collectively pledged US$1 billion. Musk resigned from the board in 2018 but remained a donor. Microsoft provided OpenAI LP with a $1 billion investment in 2019 and a second multi-year investment in January 2023, reported to be $10 billion.[10]
Better prompt:
Please summarize the following sentences to make them easier to understand.
Text: """
OpenAI is an American artificial intelligence (AI) research laboratory consisting of the non-profit OpenAI Incorporated (OpenAI Inc.) and its for-profit subsidiary corporation OpenAI Limited Partnership (OpenAI LP). OpenAI conducts AI research with the declared intention of promoting and developing a friendly AI. OpenAI systems run on the fifth most powerful supercomputer in the world.[5][6][7] The organization was founded in San Francisco in 2015 by Sam Altman, Reid Hoffman, Jessica Livingston, Elon Musk, Ilya Sutskever, Peter Thiel and others,[8][1][9] who collectively pledged US$1 billion. Musk resigned from the board in 2018 but remained a donor. Microsoft provided OpenAI LP with a $1 billion investment in 2019 and a second multi-year investment in January 2023, reported to be $10 billion.[10]
"""
另外,在吴恩达的 ChatGPT Prompt Engineering 课程中,还提到,你可以使用其他特殊符号来分割文本和 prompt,比如<>
,<tag></tag>
等,课程中的案例是这样的(注意这个是 python 代码,需要关注的是 prompt 里的 text):
text = f"""
You should express what you want a model to do by
providing instructions that are as clear and
specific as you can possibly make them.
This will guide the model towards the desired output,
and reduce the chances of receiving irrelevant
or incorrect responses. Don't confuse writing a
clear prompt with writing a short prompt.
In many cases, longer prompts provide more clarity
and context for the model, which can lead to
more detailed and relevant outputs.
"""
prompt = f"""
Summarize the text delimited by triple backticks
into a single sentence.
`{text}`
"""
3.8 信息提取:通过格式词阐述需要输出的格式
介绍完信息总结,再聊聊信息提取,我认为这个场景是继场景 3 推理以外,第二个值得深挖的场景。这个场景有非常多的有意思的场景,比如:
- 将一大段文字,甚至网页里的内容,按要求转为一个表格。
- 按照特定格式对文章内容进行信息归类。
第二个可能比较难理解,举个 OpenAI 里的例子,它的 prompt 是这样的(为了有足够空间显示内容,我仅节选了 text 里的部分内容,完整内容,可以点击这里查看):
Extract the important entities mentioned in the article below. First extract all company names, then extract all people names, then extract specific topics which fit the content and finally extract general overarching themes
Desired format:
Company names: <comma_separated_list_of_company_names>
People names: -||-
Specific topics: -||-
General themes: -||-
Text: """Powering Next Generation
Applications with OpenAI Codex
Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.
May 24, 2022
4 minute read
OpenAI Codex, a natural language-to-code system based on GPT-3, helps turn simple English instructions into over a dozen popular coding languages. Codex was released last August through our API and is the principal building block of GitHub Copilot.
Warp is a Rust-based terminal, reimagined from the ground up to help both individuals and teams be more productive in the command-line.
Terminal commands are typically difficult to remember, find and construct. Users often have to leave the terminal and search the web for answers and even then the results might not give them the right command to execute. Warp uses Codex to allow users to run a natural language command to search directly from within the terminal and get a result they can immediately use.
“Codex allows Warp to make the terminal more accessible and powerful. Developers search for entire commands using natural language rather than trying to remember them or assemble them piecemeal. Codex-powered command search has become one of our game changing features.”
—Zach Lloyd, Founder, Warp
Machinet helps professional Java developers write quality code by using Codex to generate intelligent unit test templates.
Machinet was able to accelerate their development several-fold by switching from building their own machine learning systems to using Codex. The flexibility of Codex allows for the ability to easily add new features and capabilities saving their users time and helping them be more productive.
“Codex is an amazing tool in our arsenal. Not only does it allow us to generate more meaningful code, but it has also helped us find a new design of product architecture and got us out of a local maximum.”
—Vladislav Yanchenko, Founder, Machinet"""
Prompt 有点长,我解释下,它是让 AI 将文章里的重点内容进行抽离,并将其根据特定格式进行输出。要求将文章里提到的公司、人名、主题都按照 comma separated list(按逗号空格分开)的方式罗列出来。
最后的 output 是这样的:
Company names: OpenAI, Microsoft, GitHub, Pygma, Replit, Warp, Machinet
People names: Emile Paffard-Wray, Amjad Masad, Zach Lloyd, Vladislav Yanchenko
Specific topics: GPT-3, OpenAI API, Azure OpenAI Service, GitHub Copilot, Pygma, Replit, Warp, Machinet
General themes: Natural language-to-code, Productivity, Problem solving, Creativity, Learning
技巧 6:通过格式词阐述需要输出的格式
上述的案例,就是用到了这个技巧。这个技巧是技巧 2 的变种,比较常用于生成文本场景。其实在场景 7 中,你也可以用到这个技巧。
假设你想让 AI 总结一篇非常非常长的文章,并且按照特定格式给你总结,那你可以在文章前面明确输出的格式(为了不占用太多显示空间,我省略了正文内容,你可以在 OpenAI 的 Playground 看到完整案例)。它的意思其实是让 ChatGPT 按 Topic 总结,每个 Topic 里按照无序列表(就是里面那个 -)将每个 Topic 的主要观点罗列出来。
Summarize the main points of the following speech
Use the following format:
Topic 1: <topic_name_1>
- <point_1>
..
Topic 2: <topic_name_2>
- <point_1>
..
Topic 10: ..
Text: """
Thank you so much, Fred, for that lovely introduction. And thanks to the Atlantic Council for hosting me today.
The course of the global economy over the past two years has been shaped by COVID-19 and our efforts to fight the pandemic. It’s now evident, though, that the war between Russia and Ukraine has redrawn the contours of the world economic outlook. Vladimir Putin’s unprovoked attack on Ukraine and its people is taking a devastating human toll, with lives tragically lost, families internally displaced or becoming refugees, and communities and cities destroyed.
...
"""
Output 是这样的:
Topic 1: The war in Ukraine
- The war is taking a human toll with lives lost, families displaced, and communities destroyed
- The Biden administration is committed to holding Russia accountable
- The war has violated international law and is a challenge to the international order
Topic 2: The global economy
- The war is having negative impacts on the global economy, including higher commodity prices and inflation
- The IMF and World Bank will be focused on helping developing countries weather the impacts of the war
- The ultimate outcome for the global economy depends on the path of the war
关于这个场景和技巧,我想再解释一下为什么潜力很大。根据我使用各种 Summary 或者信息提取的产品,我发现,AI 并不知道什么是重点,所以在总结的过程中,会丢失很多内容。如何引导 AI 进行总结,就变得非常重要,且具有一定的可玩性。
使用此技巧可以使输出更结构化。比如针对一篇文章进行问答,你不仅想要得到一个答案,也希望 ChatGPT 的答案符合特定的格式,方便你下一步进行自动化。
比如问 “这里的债券 duration 是多少?” , 正常 GPT 模型的答案可能是 “债券 duration 是 4 年” 或 “duration 4 年”。
ChatGPT 的回答不稳定,且不方便继续处理。
解法:
我们可以通过这个技巧,让模型理解我们预期的格式。并在此基础上,为了方便自动化,让模型输出特定的结构化答案 (比如 JSON/Markdown 等)。
也可以方便集成更多的额外要求,比如增加一个”confidence level”, 并通过 prompt 的形式指定这些数值的格式与甚至区间。
比如:
{context}
Question: What is bond duration mentioned here.
Answer template (Valid JSON format):
{{
"duration": $duration_numeric_value_in_year,
"confidence_level": $answer_confidence_level_high_moderate_or_low,
}}
Answer:
在吴恩达的 ChatGPT Prompt Engineering 课程中,有提到一个这个技巧的高级用法,在让 AI 按照特定格式输出内容的同时,还让 AI 根据内容是否满足特定条件,来判断应该输出什么结果,下面课程中的案例的 prompt(注意这个是 python 代码,有一些转义字符,可以不用管):
You will be provided with text delimited by triple quotes.
If it contains a sequence of instructions,
re-write those instructions in the following format:
Step 1 - ...
Step 2 - …
…
Step N - …
If the text does not contain a sequence of instructions,
then simply write "No steps provided."
"""{text}"""
简单解释下,这个 prompt 分成两步:
让 AI 将输入的 text 转为步骤(就是 prompt 里的 Step 1、2)
然后还增加了一个判断,如果输入的 text 里没有 step 的内容,那么就输出 No Step
假如输入的 text 是一个泡茶的步骤介绍:
Making a cup of tea is easy! First, you need to get some
water boiling. While that's happening,
grab a cup and put a tea bag in it. Once the water is
hot enough, just pour it over the tea bag.
Let it sit for a bit so the tea can steep. After a
few minutes, take out the tea bag. If you
like, you can add some sugar or milk to taste.
And that's it! You've got yourself a delicious
cup of tea to enjoy.
那么 AI 输出的内容是这样的(因为内容中包含了步骤式的内容):
Step 1 - Get some water boiling.
Step 2 - Grab a cup and put a tea bag in it.
Step 3 - Once the water is hot enough, pour it over the tea bag.
Step 4 - Let it sit for a bit so the tea can steep.
Step 5 - After a few minutes, take out the tea bag.
Step 6 - Add some sugar or milk to taste.
Step 7 - Enjoy your delicious cup of tea!
但如果我们输入的是这样的 text:
The sun is shining brightly today, and the birds are
singing. It's a beautiful day to go for a
walk in the park. The flowers are blooming, and the
trees are swaying gently in the breeze. People
are out and about, enjoying the lovely weather.
Some are having picnics, while others are playing
games or simply relaxing on the grass. It's a
perfect day to spend time outdoors and appreciate the
beauty of nature.
从内容上看,这段话,没有任何步骤式的内容,所以 AI 的输出是这样的:
No steps provided.