Your question is Low-Latency LLM Edge Integration. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you integrate a large language model into a robot's local execution pipeline while minimizing latency and computational overhead on the edge for Fieldai?
Describe the end-to-end architecture, including what runs locally versus remotely, model selection and compression, request routing, safety validation, and fallback behavior. Explain how you would evaluate latency, task success, reliability, cost, feature drift, and training-serving skew in production.