GPT-3.5 Turbo Prompt Engineering: Detailed Python Script

This script demonstrates a detailed Prompt Engineering (PT) flow using the GPT-3.5 Turbo model. It leverages the OpenAI Python library to interact with the model, generate responses, and fine-tune parameters for optimal results.

import openai

def generate_response(prompt):
    # Set OpenAI API credentials
    openai.api_key = 'YOUR_API_KEY'
    
    # Define model name and version
    model_name = 'gpt-3.5-turbo'
    
    # Set generation parameters
    max_tokens = 50  # Maximum length of generated answer
    temperature = 0.5  # Controls diversity of generated text (0 for deterministic, 1 for random)
    
    # Build the text generation request
    response = openai.Completion.create(
        engine=model_name,
        prompt=prompt,
        max_tokens=max_tokens,
        temperature=temperature,
        n=1,  # Generate only one answer
        stop=None,  # No stop token set
        echo=True  # Return both prompt and generated answer
    )
    
    # Extract the generated answer
    answer = response.choices[0].text.strip()
    return answer

# Example question and setting
prompt = 'Setting: chatgpt3.5 version is chatgpt3.5\n\nQuestion: Can you answer my question?'

# Generate answer
response = generate_response(prompt)
print(response)

This script utilizes OpenAI's Python library to interact with the GPT-3.5 Turbo model. Replace 'YOUR_API_KEY' with your OpenAI API key. Within the generate_response function, adjust generation parameters such as max_tokens and temperature as needed. The example question and setting can be modified according to your specific requirements.

Remember, this script is a template that you can customize and expand based on your project needs. It provides a fundamental framework for effective prompt engineering with GPT-3.5 Turbo.