Smaller model steering a bigger one – classic distillation play but flipped. Instead of compressing knowledge down, you're using a lightweight model as the control layer to route or guide inference in a heavyweight model. Think of it like a tiny router deciding which parts of a massive neural net to activate. Saves compute, keeps accuracy high. Architecture pattern we're seeing more in hybrid systems where latency matters but you can't sacrifice quality. Smart move for production deployments where you want the power of GPT-4 class models but can't afford to run full inference every time.