ChatGPT Prompt Engineering DeepLearningAI

Chatgpt提示工程deeplearning.ai

概述

此崩潰和免費課程在Chatgpt提示工程上由DeepLearning.AI提供，並由Openai的Andrew Ng和Isa Fulford演講。

課程計劃

• 課程1：簡介
• 課程2：指南
• 課程3：迭代
• 課程4：總結
• 課程5：推斷
• 課程6：轉換
• 課程7：擴展
• 課程8：聊天機器人
• 課程9：結論

所有筆記本示例都在實驗室文件夾中可用。

設定

加載API密鑰和相關的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' )

輔助功能

• 此功能將使使用提示更容易，並查看生成的輸出：

它使用OpenAI的gpt-3.5-turbo模型和聊天完成端點。

 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.

參考

主課程：

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

其他其他簡短的免費課程。

• https://learn.deeplearning.ai/