# 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.