### Q1: What is the Inception Score and how does it relate to the evaluation of generative models?  
### Q2: How can you use the Fréchet Inception Distance (FID) metric to improve the output realism of a generative model?  
### Q3: What are the implications of using perplexity as a metric in evaluating language models, and how can it impact model performance?  
### Q4: What are some effective strategies to ensure the training data used for generative models is diverse and representative?  
### Q5: How would you approach cleaning and preprocessing data to enhance its quality for training generative models?  
### Q6: What measures can be taken to avoid bias in generative models during the data preparation phase?  
### Q7: Can you describe a situation where neglecting continual evaluation of a generative model led to subpar results?  
### Q8: What steps would you implement to regularly assess the performance of your generative model using the discussed metrics?  
### Q9: How can incorporating user feedback into the evaluation process of generative models lead to better alignment with user needs?