### Q1: What is the basic definition of accuracy as a KPI for AI products?  
### Q2: How can you measure user satisfaction for an AI/ML product effectively?  
### Q3: What are the key differences between precision and recall in the context of AI/ML product evaluation?  
### Q4: In what ways can A/B testing be utilized to enhance the performance of an AI-driven recommendation system?  
### Q5: How does real-time analytics contribute to the continuous improvement of AI/ML products?  
### Q6: Describe the process of cohort analysis and its importance in understanding user interactions with AI products.  
### Q7: How can you apply precision metrics to improve the outcomes of a spam detection AI model?  
### Q8: What strategies can you implement to encourage user feedback on your AI/ML product?  
### Q9: Reflecting on your past experiences, how can data-driven decision-making enhance AI/ML product management?