Renowned artificial intelligence (AI) experts, Andrew Ng from DeepLearning.ai and Isa Fulford from OpenAI, have teamed up to offer an exciting new course on prompt engineering, titled “ChatGPT Prompt Engineering for Developers“. The course, which is completely free, aims to help developers better understand the prompts design and implementation for various use cases.
The ChatGPT Prompt Engineering course is specifically tailored for developers including data scientists who wish to learn more about designing prompts for different tasks including software development (coding), marketing, creating product reviews & description, writing essay, summarizing text etc. It includes several important topics such as summarizing, inferring, transforming, expanding and chatbot building. These skills are essential for developers who want to create high-quality AI applications that can respond to users in a natural and human-like way.
The course is available online at the following URL: https://learn.deeplearning.ai/chatgpt-prompt-eng/. It includes several interactive Python code examples and a comprehensive guide on how to connect with OpenAI to execute your prompts. Additionally, the course is designed in such a way that even beginners can easily follow along and start building their own prompts.
You can get started by first setting up and defining the methods such as get_completion.
pip install openai
pip install python-dotenv
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.getenv('OPENAI_API_KEY')
Once set up, you could write the code to execute your prompts. One sample code is given below:
Andrew Ng, the co-founder of DeepLearning.ai, has previously worked on several notable AI projects, including Google Brain and Baidu’s AI Group. He is also well known for his online AI courses, which have attracted millions of learners worldwide. Isa Fulford, on the other hand, is a research scientist at OpenAI, where she focuses on natural language processing and machine learning.
def get_completion(prompt, model="gpt-3.5-turbo"):
messages = [{
"role": "user",
"comtent": prompt
}]
response = openai.ChatCompletion.create(
model=model,
messages = messages,
temperature = 0 # degree of randomness
)
return response.choices[0].message["content"]
The ChatGPT Prompt Engineering course is expected to be a game-changer for developers who wish to take their AI skills to the next level. By learning the fundamentals of prompt engineering, developers can create AI models that are more accurate, efficient and user-friendly. So, if you’re a developer looking to improve your generative AI skills, don’t hesitate to check out this exciting new course today!
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I found it very helpful. However the differences are not too understandable for me