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 os
from 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。文章来源:https://www.toymoban.com/news/detail-439159.html
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 文章来源地址https://www.toymoban.com/news/detail-439159.html
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