ChatGPT Prompt Engineering DeepLearningAI
1.0.0
يتم تقديم هذا التصادم والدورة المجانية على ChatGpt Form Engineering بواسطة DeepLearning.ai ومحاضرة من قبل Andrew Ng و Isa Fulford من Openai.
جميع أمثلة دفتر الملاحظات متوفرة في مجلد المختبر.
قم بتحميل مفتاح API وبرامج Python ذات الصلة
import openai
import os
from dotenv import load_dotenv , find_dotenv
_ = load_dotenv ( find_dotenv ())
openai . api_key = os . getenv ( 'OPENAI_API_KEY' )وظيفة المساعد
يستخدم طراز gpt-3.5-turbo من Openai ونقطة الانتهاء من الدردشة.
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" ] 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 ) 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.
بالطبع الرئيسي:
آخر دورات قصيرة مجانية متوفرة على deeplearning.ai:
