Q: Should I build automated evaluators for every failure mode I find?
Focus automated evaluators on failures that persist after fixing your prompts. Many teams discover their LLM doesn’t meet preferences they never actually specified - like wanting short responses, specific formatting, or step-by-step reasoning. Fix these obvious gaps first before building complex evaluation infrastructure.
Consider the cost hierarchy of different evaluator types. Simple assertions and reference-based checks (comparing against known correct answers) are cheap to build and maintain. LLM-as-Judge evaluators require 100+ labeled examples, ongoing weekly maintenance, and coordination between developers, PMs, and domain experts. This cost difference should shape your evaluation strategy.
Only build expensive evaluators for problems you’ll iterate on repeatedly. Since LLM-as-Judge comes with significant overhead, save it for persistent generalization failures - not issues you can fix trivially. Start with cheap code-based checks where possible: regex patterns, structural validation, or execution tests. Reserve complex evaluation for subjective qualities that can’t be captured by simple rules.
This article is part of our AI Evals FAQ, a collection of common questions (and answers) about LLM evaluation. View all FAQs or return to the homepage.