---
name: tai-ch001-the-next-generation-ai-builder-will-measure-uncertaint
description: 'Apply chapter 1 of Testing AI, The Next Generation AI Builder Will Measure Uncertainty, 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 the next generation ai builder will measure uncertainty.'
---

# The Next Generation AI Builder Will Measure Uncertainty

Skill name: `tai-ch001-the-next-generation-ai-builder-will-measure-uncertaint`

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

## Purpose

Modern quality work is moving from checking single outputs to measuring behavior at scale, over
time, and through sampling. Developers who can explain uncertainty will shape how AI systems
ship.

## 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

Think of this series as a shift from checking one answer to measuring a behavior pattern. A
chatbot, recommendation engine, summarizer, fraud model, or agent can look good in one demo and
still fail too often across real traffic. The work is to measure that behavior across enough
examples to make a responsible decision. For example, one refund answer may be perfect, but the
next hundred answers may reveal policy confusion, uneven tone, and a few dangerous promises. The
next-generation AI builder sees the distribution, not just the demo.

## 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 an expert level, the main move is separating observation from inference. The sample result is
what you saw. The confidence interval is what you estimate about the wider population. The
release decision is a risk judgment that uses both, plus business context, severity,
reversibility, and monitoring plans.