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Published on 02.12.2025
TLDR: This article provides a developer-focused guide to Large Language Model (LLM) evaluations, arguing against "vibes-based development" in favor of a rigorous, data-driven workflow. It introduces a flywheel of improvement: analyze, measure, improve, and automate, using techniques like error analysis, code-based evals, and the "LLM-as-judge" pattern.