---
name: tai-ch106-appendix-how-to-read-an-ai-eval-report
description: 'Apply chapter 106 of Testing AI, Appendix: How to Read an AI Eval Report, 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 appendix: how to read an ai eval report.'
---

# Appendix: How to Read an AI Eval Report

Skill name: `tai-ch106-appendix-how-to-read-an-ai-eval-report`

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

## Purpose

Most eval reports look more precise than they are. Learn where the uncertainty is hiding.

## 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

An AI eval report is a decision artifact. It should help a reader understand what was tested,
how it was judged, how uncertain the result is, and what decision the evidence supports. A weak
report gives a score. A strong report explains the sample, the metric, the slices, the failures,
the confidence, the cost, and the risk.

## 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.

## Deeper Guidance

For serious releases, review eval reports like experimental evidence. Ask about provenance,
holdouts, multiple comparisons, judge drift, dataset drift, effect size, and practical
significance.