---
name: tai-ch040-evals-and-benchmarks
description: 'Apply chapter 40 of Testing AI, Evals and Benchmarks, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to evals and benchmarks.'
---

# Evals and Benchmarks

Skill name: `tai-ch040-evals-and-benchmarks`

Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**.

## Purpose

Benchmarks are useful signals, but many evals are narrower, noisier, or less well-defined than
their leaderboard numbers suggest.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

Evals are structured ways to measure model or system behavior. They can be public benchmarks,
private product evals, red-team suites, human preference studies, or continuous production
monitors. For example, MMLU measures broad academic knowledge, HumanEval measures code-
generation tasks, SWE-bench measures issue-resolution on real software repositories, GPQA
measures hard science questions, Chatbot Arena measures human preference, and HELM tries to
compare models across many scenarios and metrics.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Expert Notes

At expert level, audit benchmarks before trusting them. Track task validity, label quality,
contamination risk, environment drift, oracle ambiguity, metric fit, and inter-rater agreement.
A leaderboard score is an input, not a release decision.