下面是调用openai的completion接口的函数。但在本文中并不是重点。了解一下就好。
import openai
import osfrom dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())openai.api_key = os.getenv('OPENAI_API_KEY')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"]
下面我们来说说，书写提示词的基本原则。
提示词的基本原则
- 提示词的书写要清晰，带有明确的指令
- 给模型时间去思考，即指明模型的思考过程
原则一：提示词的书写要清晰，带有明确的指令
技巧一：使用分隔符清楚地指示输入的不同部分
分隔符可以是```, “”", < >, , :
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)
回答：
Clear and specific instructions should be provided to guide a model towards the desired output, and longer prompts can provide more clarity and context for the model, leading to more detailed and relevant outputs.
这个例子中需要处理的内容和处理指令是区分开的。这样便于维护。
技巧二：指明固定格式输出结果，如JSON, HTML
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)
回答：
[{"book_id": 1,"title": "The Lost City of Zorath","author": "Aria Blackwood","genre": "Fantasy"},{"book_id": 2,"title": "The Last Survivors","author": "Ethan Stone","genre": "Science Fiction"},{"book_id": 3,"title": "The Secret of the Haunted Mansion","author": "Lila Rose","genre": "Mystery"}
]
技巧三：让模型检测条件是否被满足
示例1：
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)
回答：
Completion for Text 1:
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!
这里设定的条件是，如果文本中有指令步骤就将其按步骤列出。同时，这里也写明了输出的格式，正如技巧二中提到的那样。
示例2：
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)
回答：
Completion for Text 2:
No steps provided.
示例二中的文本没有出现类似指令步骤的文字，模型也正确判断出来了。这说明模型是有推理能力的。
技巧四：少样本学习
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)
回答：
<grandparent>: Resilience is like a tree that bends with the wind but never breaks. It is the ability to bounce back from adversity and keep moving forward, even when things get tough. Just like a tree that grows stronger with each storm it weathers, resilience is a quality that can be developed and strengthened over time.
通过给模型一个示例去学习，让模型在接下来的回答中参照示例来回答。
原则二：给模型时间去思考
技巧一：指定完成任务所需的步骤
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.
"""
# example 1
prompt_1 = f"""
Perform the following actions:
1 - Summarize the following text delimited by triple \
backticks with 1 sentence.
2 - Translate the summary into Chinese.
3 - List each name in the Chinese summary.
4 - Output a json object that contains the following \
keys: chinese_summary, num_names.Separate your answers with line breaks.Text:
```{text}```
"""
response = get_completion(prompt_1)
print("Completion for prompt 1:")
print(response)
回答：
Completion for prompt 1:
1 - Siblings Jack and Jill go on a quest to fetch water from a hilltop well, but misfortune strikes as Jack trips and tumbles down the hill, with Jill following suit, yet they return home slightly battered but undeterred in their adventurous spirits. 2 - 兄妹杰克和吉尔出发去从山顶井中取水，但不幸的是，杰克绊倒了，滚下了山坡，吉尔也跟着滚下来，但他们稍微受了点伤，回到家中得到了安慰的拥抱，尽管发生了不幸，他们的冒险精神仍然不减，继续愉快地探索。3 - 杰克，吉尔。4 -
{"chinese_summary": "兄妹杰克和吉尔出发去从山顶井中取水，但不幸的是，杰克绊倒了，滚下了山坡，吉尔也跟着滚下来，但他们稍微受了点伤，回到家中得到了安慰的拥抱，尽管发生了不幸，他们的冒险精神仍然不减，继续愉快地探索。","num_names": 2
}
模型按照我们制定的步骤输出了内容。
当我们重新制定输出格式
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 Chinese.
3 - List each name in the Chinese summary.
4 - Output a json object that contains the following keys: chinese_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 for prompt 2:")
print(response)
注意：这里的text指的也是上面例子中的text。
回答：
Completion for prompt 2:
Summary: Jack and Jill go on a quest to fetch water, but misfortune strikes and they tumble down a hill, returning home slightly battered but with undimmed adventurous spirits.
Translation: Jack 和 Jill 去取水，但不幸的是他们跌倒了，回家时略有些受伤，但他们的冒险精神仍然不减，继续愉快地探索。
Names: Jack, Jill
Output JSON: {"chinese_summary": "Jack 和 Jill 去取水，但不幸的是他们跌倒了，回家时略有些受伤，但他们的冒险精神仍然不减，继续愉快地探索。", "num_names": 2}
模型真的理解了输出的格式要求，有点厉害了哦。
技巧二：指示模型在匆忙得出结论之前制定自己的解决方案
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)
回答：
The student's solution is correct.
这里学生的回答是错误的。模型却判断为正确。看样子它算数是真不好。
补救措施来了。先让模型自己找出一个方案，然后让它去和学生的解决方案进行对比。最后判断一下学生的方案是否正确。
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)
回答：
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 + 10xTotal cost: 100x + 250x + 100,000 + 10x = 360x + 100,000Is the student's solution the same as actual solution just calculated:
NoStudent grade:
Incorrect
通过这么一顿操作，模型似乎领悟到了什么。它发现了学生在Maintenance cost中错误地将10x写成了100x。
总结一下该原则，通过设置思考过程，让模型按步骤思考通常能得到更为准确的回答。
大语言模型的限制
prompt = f"""
介绍一下首飞牌电动牙刷的功能
"""
response = get_completion(prompt)
print(response)
回答：
首飞牌电动牙刷是一款高科技的电动牙刷，具有多种功能，包括：1. 高速震动清洁：首飞牌电动牙刷采用高速震动技术，每分钟可达到40000次的震动频率，能够深入清洁牙齿，有效去除牙菌斑和牙渍。2. 智能感应：该电动牙刷配备了智能感应技术，能够自动识别不同的牙齿区域，根据不同的清洁需求进行智能调节，让你的牙齿更加干净。3. 多种清洁模式：首飞牌电动牙刷提供了多种清洁模式，包括标准模式、深层清洁模式、按摩模式等，可以根据个人需求进行选择。4. 长续航时间：该电动牙刷采用高效节能的电池技术，一次充电可以使用长达30天，让你无需频繁充电。5. 人性化设计：首飞牌电动牙刷采用人性化设计，握感舒适，操作简单，适合各种年龄段的人使用。总之，首飞牌电动牙刷具有高速震动清洁、智能感应、多种清洁模式、长续航时间和人性化设计等多种功能，是一款高性能的电动牙刷，可以帮助你轻松保持口腔健康。
事实上，并没有首飞牌电动牙刷，但模型描述的挺欢快的。一本正经地胡编乱造。这就是模型表现出来的幻想。要尽量避免这种情况，就需要将提示词写的准确，清晰和指令化。
参考：
https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/2/guidelines
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我也准备了一份提示词的文档。有涉及到各个领域的提示词模板。
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