Training a generative AI model requires:

• Preparing a large, diverse dataset relevant to the desired output
• Splitting the data into training and validation sets
• Defining a loss function to measure the model’s performance
• Setting hyperparameters like learning rate and batch size
• Feeding batches of data through the model
• Calculating the loss and adjusting model weights through backpropagation
• Repeating this process for many iterations until performance plateaus
• Fine-tuning the model on specific tasks or domains if needed