ChatGPT Prompt Engineering DeepLearningAI

Chatgpt提示工程deeplearning.ai

概述

此崩溃和免费课程在Chatgpt提示工程上由DeepLearning.AI提供，并由Openai的Andrew Ng和Isa Fulford演讲。

课程计划

• 课程1：简介
• 课程2：指南
• 课程3：迭代
• 课程4：总结
• 课程5：推断
• 课程6：转换
• 课程7：扩展
• 课程8：聊天机器人
• 课程9：结论

所有笔记本示例都在实验室文件夹中可用。

设置

加载API密钥和相关的Python Libaries

 import openai
import os

from dotenv import load_dotenv , find_dotenv
_ = load_dotenv ( find_dotenv ())

openai . api_key  = os . getenv ( 'OPENAI_API_KEY' )

辅助功能

• 此功能将使使用提示更容易，并查看生成的输出：

它使用OpenAI的gpt-3.5-turbo模型和聊天完成端点。

 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" ]

用法

 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 )

完成：

 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.

参考

主课程：

• https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/1/introduction

其他其他简短的免费课程。

• https://learn.deeplearning.ai/