一、说明

这是吴恩达 《ChatGPT Prompt Engineering for Developers》 的课程笔记系列。
本文是第二讲的内容：Guidelines
课程主讲：Andrew Ng，Isa Fulford
Isa Fulford也是《OpenAI Cookbook》的主要贡献者之一

二、安装环境

1. 下载openai

pip install openai 

如果是在jupyter notebook上安装，前面需要带英文感叹号（!），之后需要执行bash指令的地方都这样

!pip install openai 

2. 导入openai，并配置openai api key

import openai
import osfrom dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())openai.api_key = os.getenv('OPENAI_API_KEY')

这里的OPENAI_API_KEY是设置在环境变量里的值，为你的openai api key
设置环境变量bash指令：

!export OPENAI_API_KEY='sk-...'		

或者，在代码中直接这样写：

openai.api_key = 'sk-...'    

3. 辅助函数
调用openai接口

def get_completion(prompt, model="gpt-3.5-turbo"):messages = [{"role": "user", "content": prompt}]response = openai.ChatCompletion.create(model=model,messages=messages,temperature=0, # this is the degree of randomness of the model's output)return response.choices[0].message["content"]

三、Guidelines

准则一：写出明确而具体的说明

方法1：使用分隔符清楚地表示输入的不同部分

分隔符可以是：

 ```,  """,  < >,  <tag> </tag>,  :   

它可以防止prompt注入，以免给模型产生混乱的理解

例子：

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}```
"""
response = get_completion(prompt)
print(response)

{text} 就是使用符合 ```分隔的内容
以上代码输出的结果为，打印的一句话总结的text的结果：

方法2：用结构化输出：如直接要求它以HTML或者JSON格式输出

例子：

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.
"""
response = get_completion(prompt)
print(response)

代码中，要求生成三个虚拟的图书，以JSON格式输出，运行结果：

方法3：请模型检查是否满足条件

要求检查：要求模型先检查是否满足某个条件后，再进行输出，如果条件不满足可以直接告知。
例子：

text_1 = f"""
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.
"""
prompt = f"""
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_1}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 1:")
print(response)

代码中，text的内容是泡一杯茶的步骤，prompt要求模型理解这段内容，告知是否能把它分解成一步一步的步骤的结构，如果能，则按照步骤描述重写，如果不能则给出则返回No steps provided。代码输出结果：

下面这个例子则给出的是不能分解成步骤的一段话：

text_2 = f"""
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.
"""
prompt = f"""
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_2}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 2:")
print(response)

方法4：Prompt中包含少量样本

例子：

prompt = f"""
Your task is to answer in a consistent style.<child>: Teach me about patience.<grandparent>: The river that carves the deepest \ 
valley flows from a modest spring; the \ 
grandest symphony originates from a single note; \ 
the most intricate tapestry begins with a solitary thread.<child>: Teach me about resilience.
"""
response = get_completion(prompt)
print(response)

代码中，给了一个child和grandparent对话的样本，要求再次按照这个样本给出grandparent的答复，运行结果：

https://blog.csdn.net/Jay_Xio/article/details/130450026

准则二：给模型一些思考的时间

方法1：指定完成任务所需的步骤

例子：

text = f"""
In a charming village, siblings Jack and Jill set out on \ 
a quest to fetch water from a hilltop \ 
well. As they climbed, singing joyfully, misfortune \ 
struck—Jack tripped on a stone and tumbled \ 
down the hill, with Jill following suit. \ 
Though slightly battered, the pair returned home to \ 
comforting embraces. Despite the mishap, \ 
their adventurous spirits remained undimmed, and they \ 
continued exploring with delight.
"""prompt_1 = f"""
Perform the following actions: 
1 - Summarize the following text delimited by triple \
backticks with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the following \
keys: french_summary, num_names.Separate your answers with line breaks.Text:
```{text}```
"""response = get_completion(prompt_1)
print("Completion for prompt 1:")
print(response)

代码中，prompt给出模型要执行任务的步骤
步骤1，用一句话总结text内容
步骤2，翻译成法语
步骤3，列出名字
步骤4，以JSON格式输出
执行代码，模型按照这个步骤输出：

prompt_2 = f"""
Your task is to perform the following actions: 
1 - Summarize the following text delimited by <> with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the following keys: french_summary, num_names.Use the following format:
Text: <text to summarize>
Summary: <summary>
Translation: <summary translation>
Names: <list of names in Italian summary>
Output JSON: <json with summary and num_names>Text: <{text}>
"""
response = get_completion(prompt_2)
print("\nCompletion

方法2：指示模型在匆忙得出结论之前制定出自己的解决方案

prompt = f"""
Determine if the student's solution is correct or not.Question:
I'm building a solar power installation and I need \help working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \ 
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations 
as a function of the number of square feet.Student's Solution:
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
"""
response = get_completion(prompt)
print(response)

代码中，要求模型判断学生的结题是否正确，运行结果：

明显第3部，MainTenance const： 应该是 100000 + 10x，而学生给出的是错误的，模型没有判断出这个步骤有误，因为它只判断了 Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000 是正确的，就给出了正确的结论。

下面的示例，要求模型先自己按照步骤一步一步给出解题步骤，然后再判断学生的解题步骤是否正确：

prompt = f"""
Your task is to determine if the student's solution \
is correct or not.
To solve the problem do the following:
- First, work out your own solution to the problem. 
- Then compare your solution to the student's solution \ 
and evaluate if the student's solution is correct or not. 
Don't decide if the student's solution is correct until 
you have done the problem yourself.Use the following format:
Question:
```
question here
```
Student's solution:
```
student's solution here
```
Actual solution:
```
steps to work out the solution and your solution here
```
Is the student's solution the same as actual solution \
just calculated:
```
yes or no
```
Student grade:
```
correct or incorrect
```Question:
```
I'm building a solar power installation and I need help \
working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations \
as a function of the number of square feet.
```
Student's solution:
```
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
```
Actual solution:
"""
response = get_completion(prompt)
print(response)

运行结果： 给出了正确解法，并判断学生的是错误的

https://blog.csdn.net/Jay_Xio/article/details/130450026

四、模型的限制

prompt = f"""
Tell me about AeroGlide UltraSlim Smart Toothbrush by Boie
"""
response = get_completion(prompt)
print(response)

要求模型介绍Boie这个公司的电动牙刷，其实这个公司不存在，产品也不存在，但是模型会煞有其事的介绍

这种模型的限制，称为模型的幻觉。

要减少这种幻觉，需要模型先从文本中找到任何相关的引用，然后请它使用这些引用来回答问题，并且把回答追溯到源文件