How do you monitor for "Concept Drift" in an LLM application? If the model's output starts getting shorter over time, is that a deployment failure or a data failure?

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?

How does reducing the precision of model weights from 16-bit to 4-bit impact your infrastructure costs?

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?