The Eval Loop: Stop Guessing, Start Measuring

This article explains how to replace guesswork with systematic evaluation when working with large language models (LLMs). It introduces the 'bakeoff' method—freezing inputs and comparing outputs across model or prompt variants—and covers key concepts like the golden set of curated queries, contract compliance vs. semantic quality, the evaluator-generator split, and three eval patterns for search, content generation, and image editing. The author emphasizes investing in eval infrastructure over prompt optimization alone, as eval methodology remains model-agnostic while prompt techniques are model-specific and expire with new releases.