### Q1: What is a low-code approach to building models using TensorFlow or PyTorch?  
### Q2: How does the dynamic computation graph feature in PyTorch enhance the model building process for finance applications?  
### Q3: What are the key differences between the high-level and low-level APIs in TensorFlow, and how do they impact model customization?  
### Q4: How can leveraging AI frameworks like TensorFlow or PyTorch lead to faster development cycles in finance products?  
### Q5: What are some specific applications of TensorFlow or PyTorch in risk assessment and fraud detection?  
### Q6: In what ways can understanding AI frameworks facilitate better collaboration between product managers and technical teams?  
### Q7: How would you explain the significance of AI frameworks to a non-technical stakeholder in your organization?  
### Q8: What strategies would you employ to communicate the technical requirements for a generative model project to your team?  
### Q9: How can effective communication skills impact the successful implementation of AI frameworks in a finance-focused project?