ChatGPT Prompt Engineering DeepLearningAI
1.0.0
Kursus crash & gratis ini di ChatGPT Prompt Engineering ditawarkan oleh deeplearning.ai dan diajar oleh Andrew Ng dan Isa Fulford dari Openai.
Semua contoh notebook tersedia di folder lab.
Muat kunci API dan libaries Python yang relevan
import openai
import os
from dotenv import load_dotenv , find_dotenv
_ = load_dotenv ( find_dotenv ())
openai . api_key = os . getenv ( 'OPENAI_API_KEY' )Fungsi pembantu
Ini menggunakan model gpt-3.5-turbo OpenAI dan titik akhir penyelesaian obrolan.
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.
Hidangan utama:
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