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

How do you track which specific feature or user in your app is driving the most "Token Spend"?

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