Standard logs store text. Why might you want to store the embeddings of your production inputs and outputs in a vector database for monitoring?

How would you implement a "Token Quota" system to prevent a single user or a bug in your code from spending $1,000 on API calls in an hour?

When switching from one model to another (let's say Llama 3 to Llama 3.1), how do you perform a Blue/Green swap? How do you handle the state of ongoing "streaming" conversations during the switch?

You are running a high-volume AI application. You notice that 15% of your costs come from 'Refinement Loops' where the model has to correct its own initial mistakes. How do you architect a 'Data Flywheel' to reduce these costs over time, and how do you handle the 'Data Contamination' risk of training a model on its own synthetic outputs?