LLM Evaluation Readiness Checklist

When to Use This Checklist

Use this checklist before you rely on an LLM-powered feature in production or change its prompt or model. Evaluation, often called evals, is the systematic measurement of output quality against fixed criteria. Without it, teams ship changes on vibes and discover regressions only through user complaints. This checklist ensures an eval harness exists and is trustworthy.

How to Use This Checklist

Define quality criteria and thresholds tied to the actual use case, then build a representative dataset that includes edge, adversarial, and safety cases. Pick an evaluation method and, critically, validate any automated judge against human labels so you trust its scores. Wire evals into CI so every prompt or model change is measured, and add a regression gate that blocks releases below threshold. Version datasets and results so comparisons are reproducible over time.

What Good Looks Like

A ready evaluation harness measures quality on a versioned, representative dataset that includes safety cases. Automated scorers agree closely with human judgment. Evals run in CI on every change, report quality, cost, and latency, and a gate blocks regressions. Production feedback continuously expands the dataset, and known coverage gaps are documented so stakeholders understand the limits of the metric.

Common Pitfalls

The biggest pitfall is trusting an LLM-as-judge without validating it against humans, which produces confident but wrong scores. Eval sets that omit edge and adversarial cases give a false sense of safety. Running evals manually means they are skipped under pressure. Unversioned datasets make results incomparable across releases. Finally, ignoring cost and latency in evals hides expensive regressions.

Related Resources

Review LLM evaluation, observability, prompt engineering, guardrails, and hallucination mitigation practices.