---
name: tai-ch033-cost-latency-and-quality-tradeoffs
description: 'Apply chapter 33 of Testing AI, Cost, Latency, and Quality Tradeoffs, 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 cost, latency, and quality tradeoffs.'
---

# Cost, Latency, and Quality Tradeoffs

Skill name: `tai-ch033-cost-latency-and-quality-tradeoffs`

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

## Purpose

A model can be smarter, slower, safer, riskier, cheaper, and more expensive all at the same
time. Quality decisions need the whole picture.

## 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

AI quality is rarely a single metric. A change can improve answer quality while increasing
latency, cost, token use, tool calls, or escalation rate. For example, a larger model may raise
average score from 8.0 to 8.4 but double cost and push p95 latency beyond the product target.
The quality number alone does not decide the release.

## 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

Expert teams build Pareto views: quality, safety, latency, cost, and escalation rate. A release
candidate is not automatically best because it wins one metric; it is best when it sits on the
right frontier for the product's risk and economics.