---
name: tai-theme-quality-metrics-and-improvement-curves
description: 'Use the Testing AI theme Quality Metrics and Improvement Curves to plan, review, or teach related AI quality work. Applies concepts and techniques from the book to testing AI, AI-generated software, and non-deterministic systems when relevant.'
---

# Quality Metrics and Improvement Curves

Skill name: `tai-theme-quality-metrics-and-improvement-curves`

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

## Theme Purpose

Use these approaches when building 0-1 quality metrics, weighting sub-scores, using confidence intervals for release decisions, and explaining asymptotic AI quality curves.

Apply these concepts when testing AI, AI-generated software, model-backed features, agents, search, chatbots, RAG systems, generated code, dynamic interfaces, or other software whose behavior can vary across runs, users, data, tools, or time.

## How To Use This Theme

- Identify the behavior, capability, risk, or release decision being evaluated.
- Choose the relevant concepts below and turn them into concrete eval cases, samples, traces, checks, rubrics, metrics, or release gates.
- Prefer evidence that supports a decision: ship, canary, hold, rollback, or collect more samples.
- Report by slices and severe failures when averages hide risk.
- Preserve enough evidence that another person or agent can understand what was tested, how it was measured, and why the recommendation follows.

## Concepts And Techniques To Apply

- Build explicit 0-1 quality metrics from weighted sub-scores that match product value and risk.
- Use confidence intervals around quality metrics before deciding whether to release.
- For search, use NDCG or similar graded relevance metrics when ranking position matters.
- Expect AI quality curves to improve quickly at first, then taper asymptotically, with perfection remaining out of reach.

## Reporting Guidance

- State what was tested and what population the evidence represents.
- Explain uncertainty, missing coverage, severe failures, and known blind spots.
- Connect findings to a concrete decision or next action.
- Use topic-specific chapter skills only when deeper detail is needed; this theme skill should stand alone as practical guidance.