ChatGPT Prompt Engineering DeepLearningAI
1.0.0
Этот Crash & Free Course of Catgpt Quick Engineering предлагается Deeplearning.ai и лекция Andrew Ng и Isa Fulford из Openai.
Все примеры ноутбуков доступны в лабораторной папке.
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import openai
import os
from dotenv import load_dotenv , find_dotenv
_ = load_dotenv ( find_dotenv ())
openai . api_key = os . getenv ( 'OPENAI_API_KEY' )Вспомогательная функция
Он использует модель gpt-3.5-turbo от OpenAI и конечную точку завершения чата.
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.
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