GPT-4 generates its own system messages for a given goal and auto evaluates them

Introduction

In this tutorial, we will explore how GPT-4 can generate its own system messages tailored for specific goals and automatically evaluate their effectiveness. This process is essential for enhancing AI interactions and optimizing performance in various applications. By following the steps outlined here, you will learn how to implement this functionality using basic and dynamic coding techniques, along with a Streamlit UI.

Step 1: Setting Up Your Environment

To begin, ensure you have the necessary tools and libraries installed. Follow these steps:

1. Install Python: Make sure you have Python installed on your machine. You can download it from python.org.
2. Install Streamlit: Open your terminal or command prompt and run the following command:

   pip install streamlit
   

3. Set Up Project Files: Download the basic project files from the Patreon links provided. This will give you a foundation to work from.

Step 2: Understanding Basic Static Code

Next, familiarize yourself with the basic static code that GPT-4 utilizes to generate system messages.

1. Review the Code Structure:

   • Look for functions that handle input and output.
   • Identify how system prompts are structured to achieve specific goals.

2. Key Elements:

   • Input Handling: Understand how user inputs are captured and processed.
   • Output Generation: Review how GPT-4 crafts responses based on the input.

Step 3: Exploring Basic Dynamic Code

Dynamic code enhances the adaptability of your system messages. Follow these steps:

1. Dynamic Inputs: Modify the input handling to accept different types of data dynamically.

2. Functionality Enhancements:

   • Introduce conditions that adjust the responses based on user context.
   • Use loops or conditionals to refine the message generation process.

3. Sample Code Snippet: Here’s a simple example of dynamic input handling:

   def generate_message(user_input):
       if "help" in user_input:
           return "How can I assist you today?"
       else:
           return "Please provide more details."
   

Step 4: Streamlit UI Code Review

To create an interactive user experience, you'll implement a Streamlit UI. Follow these steps:

1. Build the UI:

   • Use Streamlit components like st.text_input() for user inputs.
   • Display outputs with st.write().

2. Sample UI Code: Here’s how you can set up a basic Streamlit app:

   import streamlit as st
   
   st.title("GPT-4 System Message Generator")
   user_input = st.text_input("Enter your request:")
   if st.button("Generate"):
       message = generate_message(user_input)
       st.write(message)
   

3. Running the Streamlit App:

   • Save your code in a file named app.py.
   • Run the app using the command:

   streamlit run app.py
   

Step 5: Final Demo and Evaluation

Once your application is running, evaluate its performance:

1. Testing: Input various prompts to see how GPT-4 responds.
2. Feedback Loop: Implement a mechanism to collect user feedback on generated responses to improve the system iteratively.

Conclusion

In this tutorial, we covered the essentials of setting up a system that allows GPT-4 to generate and evaluate its own system messages. By understanding static and dynamic coding techniques and integrating a Streamlit UI, you can create a responsive and interactive application. For further development, consider exploring more advanced features or integrating additional AI capabilities. Happy coding!