Part II - Defining Metrics for Success Using LLMs

Moving beyond the subjective “vibe check” is the critical step in maturing from a hobbyist experiment to a production-grade “Small AI” system. While operational metrics like Time to First Token (TTFT) and Tokens Per Second (TPS) validate the latency and “snappiness” advantages of running local models on consumer hardware, they must be rigorously balanced against quality standards to ensure utility isn’t sacrificed for speed. By implementing the “LLM-as-a-Judge” pattern, where capable frontier models automatically grade the outputs of efficient local models and monitoring RAG-specific health indicators like context precision and faithfulness, developers can quantify exactly what is gained in privacy and cost versus what is traded in raw capability. This shift to data-driven evaluation allows us to make good choices in model and platform selections. We are not just building models that function, but engineering systems that win on the specific margins that drive business value.