### Q1: What is the primary function of the input layer in a neural network?  
### Q2: How does the number of hidden layers in a neural network affect its ability to learn complex patterns?  
### Q3: What adjustments are made during the training of neural networks to minimize the difference between predicted and actual outputs?  
### Q4: What are some potential ways to improve the quality of training data used for generative AI models in your organization?  
### Q5: How can having a diverse dataset enhance the performance of a generative AI model in predicting customer service needs?  
### Q6: Why is having a large volume of past customer interactions beneficial for training a generative AI model?  
### Q7: In what scenarios would Generative Adversarial Networks (GANs) be particularly useful for a fintech company?  
### Q8: How might Variational Autoencoders (VAEs) assist in generating marketing content for your business?  
### Q9: What communication strategies could be employed to explain the importance of data preprocessing to team members unfamiliar with generative AI?