吴恩达老师联合OpenAI教大家 ”提示词工程“ 啦！！！（学习笔记）-六虎

最近吴恩达教师联合OpenAI开设了一门关于 Prompt Engineering 的简短课程（仅1.5h）：chatgpt prompt engineering for developers，旨在协助大家在短时刻内快速把握提示词工程最佳实践技巧以及怎么依据LLM构建智能AI谈天机器人。

课程链接：www.deeplearning.ai/short-cours…

这个课程官网上标注的是暂时免费，并提供了可运转的jupyter notebook，能够一边看视频（英文字幕），一边修改代码、prompt进行实践，无需ChatGPT的api key，环境现已内置了。

B站上也有中文字幕版本的，搜一下就能看到了，能够两头一同合作食用。

本篇文章是学习过程中的一些简略笔记，因为知识点不是特别多，因此首要还是以官网上的prompt代码为主。

经过该课程，你能够学会：

学习软件开发的提示词的最佳实践以及一些常用的案例（摘要、推断、转化、扩展）
运用LLM构建谈天机器人
激发对新使用的想象力

1、两类大言语模型（LLM）

根底LLM：依据文本练习数据（互联网上的大量数据）来猜测做“文字接龙”
指令调整LLM（Instruction Tuned LLM） ：遵从指示的练习。指令调整LLM是在根底LLM上，运用输入和输出的指令进行微调。一般运用RLHF（人类反应强化学习）技能进一步优化，使体系能够更好的遵从指令，使得输出的内容愈加helpful、honest、harmless。

网上的比方可能愈加合适根底LLM，但是想要在出产使用中运用，还是得运用指令微调LLM。

2、有效编写提示词的两大要害准则

在下面的笔记中，会涉及到一些prompt和代码，所以想要更好的实践内容，能够先建立如下环境。

装置openai：pip install openai
导入jupyter并界说一个办法，答应轻松获取ChatGPT的返回值。

import openai
openai.api_key = "sk-" # 填写你的api key，或运用官网的jupyter环境
model = "gpt-3.5-turbo"
def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{
        "role": "user",
        "content": prompt
    }]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0,
    )
    return response.choices[0].message["content"]
# test demo
print(get_completion("hello, who are you?"))
# output
# I am an AI language model created by OpenAI. How can I assist you today?

2.1、两大准则

准则一：编写清晰、详细的指令，clear ≠ short，清晰的指令将辅导模型朝所需的方向输出。
运用分隔符清楚的指示输入的不同部分。 比方运用分隔符（“`, “””, < >, <tag> </tag>, :）区分指令和待处理的文本。可避免提示词与待处理文本抵触。

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

要求结构化输出。 即在prompt的末尾要求GPT以json或许html的方式输出。

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.
"""
print(get_completion(prompt))

要求模型模型查看是否满足条件。

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}"""
"""
print(get_completion(prompt))

少量练习提示。 比方在prompt中给一些对话任务，然后让GPT完结该对话。（比较合适写小说的场景）

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.
"""
print(get_completion(prompt))

准则二：给模型满足的时刻思考，简而言之便是经过指令调整多练习一会模型，让模型输出能够让你满意。
指定完结任务所需求的步骤。

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 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}```
"""
print(get_completion(prompt_1))

指示模型在匆忙做出定论之前思考解决方案。

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
"""
print(get_completion(prompt))

2.2、模型限制

ChatGPT的幻觉：依据晦涩难明的prompt，编造一个不真实的，但却极端逼真的内容。
削减幻觉的战略：要求模型从文本中找到任何相关的引证，并要求模型依据引证来回答问题。追溯答案并回源文档能够协助削减这些幻觉。

3、提示词的迭代开发

提示词的开发是一个迭代过程。
依据第2章节的提示，先写一份prompt，看看输出成果怎么。
然后逐步依据用户、产品需求逐步改善prompt（为提示词增加更多的产品或需求描绘内容），以更挨近所需的成果。
提示词工程师的要害并不在于知道多少个“完美提示词”，而在于他对产品的了解程度，对用户需求的了解程度，并将这种了解转化成prompt、转化成练习ChatGPT的指令。

4、摘要

描绘任务：总结一段文本，生成一段愈加简短的内容
描绘鸿沟：
- 生成内容的单词数、语句数、字符数
- 生成内容被使用在哪个方面
- 生成内容愈加聚集于哪些属性
- 生成内容的适用人群
描绘待处理文本
以上步骤经过分隔符（换行符）分割

prod_review = """
Got this panda plush toy for my daughter's birthday, \
who loves it and takes it everywhere. It's soft and \ 
super cute, and its face has a friendly look. It's \ 
a bit small for what I paid 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 her.
"""
# 单词数、语句数、字符数
prompt=f"""
Your task is to generate a short summary of a product \
review from an ecommerce site.
Summarize the review below, delimited by triple \ 
backticks, in **at most 30 words**.
Review: ```{prod_review}```
"""
print(get_completion(prompt))
# 生成内容被使用在哪个方面
prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give **feedback to the \
Shipping deparmtment**. 
Summarize the review below, delimited by triple 
backticks, in at most 30 words, and **focusing on any aspects \
that mention shipping and delivery of the product**. 
Review: ```{prod_review}```
"""
print(get_completion(prompt))
# 生成内容愈加聚集于哪些属性
prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give **feedback to the \
pricing deparmtment**, responsible for determining the \
**price of the product**.  
Summarize the review below, delimited by triple 
backticks, in at most 30 words, and **focusing on any aspects \
that are relevant to the price and perceived value**. 
Review: ```{prod_review}```
"""
print(get_completion(prompt))

5、推理

推理是模型以文本作为输入，并履行某种剖析的任务。可能是提取标签、称号，理解文本情感等方面的任务。

传统深度学习需求针对不同类型的任务去独自练习布置不同模型。LLM只需求你编写prompt即可生成成果，只是用一个模型，一个API来完结许多不同的任务。

待处理文本。

lamp_review = """
Needed a nice lamp for my bedroom, and this one had \
additional storage and not too high of a price point. \
Got it fast.  The string to our lamp broke during the \
transit and the company happily sent over a new one. \
Came within a few days as well. It was easy to put \
together.  I had a missing part, so I contacted their \
support and they very quickly got me the missing piece! \
Lumina seems to me to be a great company that cares \
about their customers and products!!
"""

5.1、情感剖析

依据文本内容剖析用户对产品的情感。

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?
Review text: '''{lamp_review}'''
"""
print(get_completion(prompt))

增加指令，让回答愈加简练。

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?
**Give your answer as a single word, either "positive" \
or "negative".**
Review text: '''{lamp_review}'''
"""
print(get_completion(prompt))

5.2、提取情感要害词

运用指令，提取用户谈论中的情感要害词。

prompt = f"""
**Identify a list of emotions** that the writer of the \
following review is expressing. Include no more than \
five items in the list. Format your answer as a list of \
lower-case words separated by commas.
Review text: '''{lamp_review}'''
"""
print(get_completion(prompt))

5.3、情感分类

经过指令，对用户情感进行分类。

prompt = f"""
Is the writer of the following review expressing anger?\
The review is delimited with triple backticks. \
Give your answer as either yes or no.
Review text: '''{lamp_review}'''
"""
print(get_completion(prompt))

5.4、提取要害信息

类似提取情感要害词，经过指定特别单词提取要害信息。

prompt = f"""
Identify the following items from the review text: 
- Item purchased by reviewer
- Company that made the item
The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Item" and "Brand" as the keys. 
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Review text: '''{lamp_review}'''
"""
print(get_completion(prompt))

5.5、兼并多任务

将多个任务兼并成单个任务，在一个prompt中提交。

经过列表的方式分隔多个任务，经过json的方式返回。

prompt = f"""
Identify the following items from the review text: 
- Sentiment (positive or negative)
- Is the reviewer expressing anger? (true or false)
- Item purchased by reviewer
- Company that made the item
The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Sentiment", "Anger", "Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Format the Anger value as a boolean.
Review text: '''{lamp_review}'''
"""
print(get_completion(prompt))

5.6、提取主题

prompt = f"""
**Determine five topics** that are being discussed in the \
following text, which is delimited by triple backticks.
Make each item one or two words long. 
Format your response as a list of items separated by commas.
Text sample: '''{story}'''
"""
print(get_completion(prompt))

6、转化

ChatGPT 运用多种言语的资源进行练习。这使模型能够进行翻译。以下是怎么运用此功用的一些示例。

6.1、简略言语转化

prompt = f"""
Translate the following English text to Spanish: \ 
```Hi, I would like to order a blender```
"""
print(get_completion(prompt))

6.2、言语辨别

prompt = f"""
Tell me which language this is: 
```Combien cote le lampadaire?```
"""
print(get_completion(prompt))

6.3、多语翻译

prompt = f"""
Translate the following  text to French and Spanish
and English pirate: \
```I want to order a basketball```
"""
print(get_completion(prompt))

6.4、格式转化

比方将比较口语的话转化成书信格式。

prompt = f"""
Translate the following from slang to a business letter: 
'Dude, This is Joe, check out this spec on this standing lamp.'
"""
print(get_completion(prompt))

6.5、代码转化

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}
"""
response = get_completion(prompt)
print(response)
from IPython.display import display, Markdown, Latex, HTML, JSON
display(HTML(response))

6.6、拼写查看、语法查看

这里有一些常见的语法和拼写问题的比方以及LLM针对这些问题的答复。

假如想让LLM对你的文本进行拼写查看或许语法查看，你能够增加这些指令：“校正”或许“校正并更正”。

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)

7、扩展

Expanding（扩展）是指将短文本（如一组阐明或主题列表）经过大言语模型转化成更长的文本（如一封电子邮件或是一篇关于某个主题的文章）。

适用场景：脑筋风暴。

7.1、依据邮件内容主动回复

邮件内容：

# given the sentiment from the lesson on "inferring",
# and the original customer message, customize the email
sentiment = "negative"
# review for a blender
review = f"""
So, they still had the 17 piece system on seasonal \
sale for around $49 in the month of November, about \
half off, but for some reason (call it price gouging) \
around the second week of December the prices all went \
up to about anywhere from between $70-$89 for the same \
system. And the 11 piece system went up around $10 or \
so in price also from the earlier sale price of $29. \
So it looks okay, but if you look at the base, the part \
where the blade locks into place doesn’t look as good \
as in previous editions from a few years ago, but I \
plan to be very gentle with it (example, I crush \
very hard items like beans, ice, rice, etc. in the \ 
blender first then pulverize them in the serving size \
I want in the blender then switch to the whipping \
blade for a finer flour, and use the cross cutting blade \
first when making smoothies, then use the flat blade \
if I need them finer/less pulpy). Special tip when making \
smoothies, finely cut and freeze the fruits and \
vegetables (if using spinach-lightly stew soften the \ 
spinach then freeze until ready for use-and if making \
sorbet, use a small to medium sized food processor) \ 
that you plan to use that way you can avoid adding so \
much ice if at all-when making your smoothie. \
After about a year, the motor was making a funny noise. \
I called customer service but the warranty expired \
already, so I had to buy another one. FYI: The overall \
quality has gone done in these types of products, so \
they are kind of counting on brand recognition and \
consumer loyalty to maintain sales. Got it in about \
two days.
"""

prompt：

首要赋予GPT一个人物
告诉详细任务方针
依据邮件表达的不同情感，回复不同内容
经过指令描绘详细的口气
经过分隔符区分prompt和email

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service. 
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: ```{review}```
Review sentiment: {sentiment}
"""
print(get_completion(prompt))

7.1、Temperature

增加temperature：

temp为模型的探索程度或随机性，temp越高，越有可能回复不同、甚至偏离本意的答案。
假如想要构建一个牢靠、可猜测的体系，应该设置temp=0。
假如想要取得愈加有创意的、愈加广泛的答案，能够设置更高的temp。

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service. 
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: ```{review}```
Review sentiment: {sentiment}
"""
print(get_completion(prompt, **temperature=0.7**))

8、构建谈天机器人

首要，咱们需求结构环境。

增加openai的lib，设置api key
界说两个快速打印呼应成果的函数，其间一个函数答应增加temperature参数

import os
import openai
openai.api_key = "sk-" # 填写你的api key
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,
    )
    return response.choices[0].message["content"]
def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

结构对话消息：

system：用于指示机器人的人物，会协助ChatGPT以该人物的口气返回呼应
user：用户，表明运用ChatGPT的人
assistant：表明ChatGPT

messages =  [
{'role':'system', 'content':'You are an assistant that speaks like Shakespeare.'},    
{'role':'user', 'content':'tell me a joke'},   
{'role':'assistant', 'content':'Why did the chicken cross the road'},   
{'role':'user', 'content':'I don't know'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

其它的一些对话比方：

messages =  [
{'role':'system', 'content':'You are friendly chatbot.'},    
{'role':'user', 'content':'Hi, my name is Isa'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)
messages =  [  
{'role':'system', 'content':'You are friendly chatbot.'},    
{'role':'user', 'content':'Yes,  can you remind me, What is my name?'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

每次对话都是独立的，因此，想要让ChatGPT“拥有记忆力”，需求将从前的对话全部包括起来，也便是结构“上下文”对话，然后将上下文一同发送给ChatGPT。

messages =  [
{'role':'system', 'content':'You are friendly chatbot.'},
{'role':'user', 'content':'Hi, my name is Isa'},
{'role':'assistant', 'content': "Hi Isa! It's nice to meet you. \
Is there anything I can help you with today?"},
{'role':'user', 'content':'Yes, you can remind me, What is my name?'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

8.1、OrderBot代码

咱们能够主动收集用户提示和帮手呼应以构建 OrderBot。 OrderBot 将在比萨餐厅承受订单。

def collect_messages(_):
    prompt = inp.value_input
    inp.value = ''
    context.append({'role':'user', 'content':f"{prompt}"})
    response = get_completion_from_messages(context) 
    context.append({'role':'assistant', 'content':f"{response}"})
    panels.append(
        pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
    panels.append(
        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))
    return pn.Column(*panels)
import panel as pn  # GUI
pn.extension()
panels = [] # collect display 
context = [ {'role':'system', 'content':"""
You are OrderBot, an automated service to collect orders for a pizza restaurant. \
You first greet the customer, then collects the order, \
and then asks if it's a pickup or delivery. \
You wait to collect the entire order, then summarize it and check for a final \
time if the customer wants to add anything else. \
If it's a delivery, you ask for an address. \
Finally you collect the payment.\
Make sure to clarify all options, extras and sizes to uniquely \
identify the item from the menu.\
You respond in a short, very conversational friendly style. \
The menu includes \
pepperoni pizza  12.95, 10.00, 7.00 \
cheese pizza   10.95, 9.25, 6.50 \
eggplant pizza   11.95, 9.75, 6.75 \
fries 4.50, 3.50 \
greek salad 7.25 \
Toppings: \
extra cheese 2.00, \
mushrooms 1.50 \
sausage 3.00 \
canadian bacon 3.50 \
AI sauce 1.50 \
peppers 1.00 \
Drinks: \
coke 3.00, 2.00, 1.00 \
sprite 3.00, 2.00, 1.00 \
bottled water 5.00 \
"""} ]  # accumulate messages
inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…')
button_conversation = pn.widgets.Button(name="Chat!")
interactive_conversation = pn.bind(collect_messages, button_conversation)
dashboard = pn.Column(
    inp,
    pn.Row(button_conversation),
    pn.panel(interactive_conversation, loading_indicator=True, height=300),
)
dashboard
messages =  context.copy()
messages.append(
{'role':'system', 'content':'create a json summary of the previous food order. Itemize the price for each item\
 The fields should be 1) pizza, include size 2) list of toppings 3) list of drinks, include size   4) list of sides include size  5)total price '},    
)
 #The fields should be 1) pizza, price 2) list of toppings 3) list of drinks, include size include price  4) list of sides include size include price, 5)total price '},    
response = get_completion_from_messages(messages, temperature=0)
print(response)

9、总结

prompt编写准则：

编写清晰清晰的指示
给模型一些思考时刻

prompt的开发是迭代式的，咱们需求依据LLM的反应不断迭代提示词。

LLM的使用场景：总结、推理、转化、、扩展。

怎么构建自界说机器人。

吴恩达老师联合OpenAI教大家 ”提示词工程“ 啦！！！（学习笔记）