LLM Evals

LLM systems are variable enough that a few spot checks can look good while real failures still grow elsewhere. If quality is judged only by manual impressions, regressions hide easily, especially in specific user slices or failure categories that do not show up in a small demo set.

Early teams often changed prompts or models and checked a few examples by hand. As systems became more complex and changed more frequently, that approach failed to catch regressions. Evals emerged as the practical replacement for intuition-only quality checks.

A team defines a representative dataset and scoring criteria that reflect real failure modes. Candidate systems are run on that set, and outputs are scored using exact rules, model judges, human review, or a combination. The most useful result is often not the average score, but the slices, specific subsets of requests, that reveal where the system still breaks.

Evals, observability, and guardrails all deal with failures, but they act at different times. If you need a controlled before-and-after comparison before release, use evals. If you need to understand why production is failing now, use observability. If you need to block or soften failures at runtime, use guardrails. Strong eval scores still do not guarantee that new live traffic will behave the same way.

Teams use evals before prompt changes, retrieval tuning, model routing changes, and safety updates. Average metrics alone are rarely enough. The better practice is to keep adding newly discovered production failures into the eval set so the test bed stays aligned with reality.