/ChatGPT-Prompt-Engineering

Primary LanguageJupyter Notebook

ChatGPT-Prompt-Engineering

Credit

The notebooks follow the free lessons offered in the youtube series by Isa Fulford and Andrew Ng, although I sometimes use different examples. There is also a corresponding DeepLearning.AI course on the topic of prompt engineering. They cover best practices for prompt engineering and demonstrate how to use LLM APIs for a range of tasks, including:

  • Summarization (e.g., condensing user reviews for conciseness)
  • Inference (e.g., sentiment analysis, topic extraction)
  • Text transformation (e.g., translation, spelling and grammar correction)
  • Text expansion (e.g., automated email composition)

Requirements

To set up the project and use the notebooks, follow these requirements:

  1. Make sure you have Python installed on your system (Python >= 3.6).

  2. Install the required Python packages using pip:

    pip install openai python-dotenv redlines

  3. Create a .env file in the project directory.

  4. Add your OpenAI API key to the .env file as follows:

    OPENAI_API_KEY="your_openai_api_key_here"

    Replace "your_openai_api_key_here" with your actual OpenAI API key.

  5. Save the .env file.

  6. In your Python script, import the necessary libraries and configure the OpenAI API client using the dotenv package:

    import openai
import os
from dotenv import load_dotenv, find_dotenv

# Load environment variables from .env file
load_dotenv(find_dotenv())

# Configure the OpenAI API client with your API key
openai.api_key = os.getenv("OPENAI_API_KEY")

Additional Resources