What Do You Need to Prepare for Fine-Tuning?

Before fine-tuning an AI model, you'll need well-prepared data that represents the task you want the AI to perform. Here's a breakdown of the inputs and outputs to prepare:

1. Input Data (A column in Google Sheets)

   • Description: Examples of the data the AI will process.
   • Examples: Text snippets, product descriptions, customer inquiries, or other raw data.
   • Format: Input data should be organized in a column format, which can easily be copied into Google Sheets

2. Desired Output (A column in Google Sheets)

   • Description: The expected result for each input.
   • Examples:
     • Input: "Promote our new eco-friendly bottles." Output: "Introducing our eco-friendly bottles – stylish, sustainable, and perfect for daily use!"
     • Input: "Men's running shoes" Output: "Product Category: Sportswear"
   • Format: Output data should also be in a column format, aligning with the input data.

3. Data Format

   • Description: A structured dataset where each row or entry maps an input to its corresponding output.
   • Structure: Data can be in any format that can be copied as columns in Google Sheets. Ensure you have high-quality data, as your AI will learn from it.

4. Adequate Data Volume

   • Recommendation: You can fine-tune a model with as few as 20 examples. However, more data generally improves the quality of the responses. Aim for 100 to 500 examples, depending on your application.

5. Clear Objectives

   • Description: A clear understanding of what you want the AI to achieve, whether it’s generating text in a specific tone or categorizing products.

By following these guidelines, you can effectively prepare your data for fine-tuning an AI model, ensuring that it meets your specific needs and performs accurately.