How to Build a Generative Model with Promptrepo?

Generative models generate new outputs based on learned patterns. In this tutorial, we’ll create a model that generates nutritional estimates for food items when real data is unavailable.

Step 1: Preparing Training Data

For generation, our dataset consists of:

  • Web page title: The name of the Web page.
  • Web page content: The content of the web page.
  • Nutritional category: Category of the nutrition such as beverages, general food etc.
  • Ingredients: Source ingredients of the food product.
  • Nutrition per serving: Nutritional values per serving of the food item.
  • Serving size (grams or mL): size of one serving of the food item
  • Nutrition information available in the input: the nutritional values of the food available in the input
  • Guessed nutrition: the nutritional information of the food that weren't given and was predicted instead

Step 2: Uploading Data to Promptrepo

  1. Open Google Sheets and add or import the training dataset
  2. Click Extensions > Promptrepo > Train AI
  3. Promptrepo sidebar widget will be displayed
  4. Enter the AI model name and click Next
  5. Select Web page title & Web page content as input columns and click Next
  6. Choose all other columns as output columns and click Next

Step 3: Training & Testing the Model

  1. Click Publish and let Promptrepo fine tune the generative model
  2. Once trained, test the model by inputting a food name and its description
  3. The model should extract structured data (ingredients & nutrition) from the input text. If real data is missing, the model will generate a reasonable estimate based on similar foods.

Generative models are powerful for filling gaps where real data is unavailable. With Promptrepo, you can fine-tune AI models for classification, extraction, and generation with ease. Whether you are structuring unorganized data, extracting key details, or generating insights, Promptrepo makes AI training accessible and efficient.