model fine-tuning

When using GPT for generating personalized recommendations for home improvement projects, consider factors such as data quality, model fine-tuning, user feedback incorporation, and ethical considerations. It is important to ensure that the data used to train the GPT model is relevant and accurate to yield reliable recommendations. Fine-tuning the model based on specific user preferences can enhance the quality of recommendations. Incorporating user feedback helps refine the recommendations over time. Additionally, ethical considerations such as privacy and bias must be carefully addressed to prioritize user trust and fairness.