ChatGPT Prompt Engineering DeepLearningAI
1.0.0
Dieser Crash & Free -Kurs in Chatgpt ProMP -Engineering wird von DeepLearning.ai angeboten und von Andrew Ng und Isa Fulford von Openai unterrichtet.
Alle Beispiele für Notebooks sind im Laborordner verfügbar.
Laden Sie den API -Schlüssel und die relevanten 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' )Helferfunktion
Es verwendet gpt-3.5-turbo -Modell von OpenAI und den Endpunkt des Chat-Abschlusses.
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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