Explain how Continuous Batching (used in engines like vLLM) differs from traditional static batching. How does it improve GPU utilization?

You have a task that requires complex reasoning 10% of the time and simple extraction 90% of the time. How do you architect a "Router" to save 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?

When is it more cost-effective to use a "Pay-per-token" API (like OpenAI) versus hosting your own model on a dedicated cloud instance (like an AWS g5 instance)?