Building Personal AI Benchmarks for Model Selection
Published AI benchmarks are often saturated and irrelevant to specific workflows. This analysis outlines a five-step framework for creating personal, blind-tested benchmarks to evaluate LLMs based on real-world utility, cost, and latency. It provides actionable strategies for operators to optimize their AI stacks amidst rapid model releases.