---
name: tai-ch182-performance-engineering-for-ai-systems
description: 'Apply chapter 182 of Testing AI, Performance Engineering for AI Systems, 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 performance engineering for ai systems.'
---

# Performance Engineering for AI Systems

Skill name: `tai-ch182-performance-engineering-for-ai-systems`

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

## Purpose

Performance is no longer just a test at the end. For AI systems, it is an engineering discipline
tied directly to quality, cost, reliability, and trust.

## 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

Performance engineering for AI systems measures how the system behaves under real traffic, real
dependencies, real model routes, and real user expectations. It is not only about whether a page
loads quickly or a model returns before a timeout. It is about whether the product can deliver
trustworthy AI behavior within acceptable latency, cost, throughput, reliability, and user-
experience limits.

## 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 high-confidence decisions, performance engineering for AI systems should instrument every
span: prompt construction, retrieval, reranking, model call, tool call, judge pass, retry, cache
lookup, fallback, streaming, and final rendering. Report p50, p95, p99, throughput, queue depth,
timeout rate, retry rate, cache hit rate, provider errors, cost per successful task, and quality
per second. Treat performance regressions as quality regressions when they change user trust,
safety, business value, or release risk.