A Pragmatic Guide to LLM Evals for Developers

A pragmatic guide to LLM evals for devs

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.

A pragmatic guide to LLM evals for devs