---
name: tai-ch071-voice-and-multimodal-ai-testing
description: 'Apply chapter 71 of Testing AI, Voice and Multimodal AI Testing, 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 voice and multimodal ai testing.'
---

# Voice and Multimodal AI Testing

Skill name: `tai-ch071-voice-and-multimodal-ai-testing`

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

## Purpose

Voice and multimodal systems add new failure modes before the model even starts reasoning.

## 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

Voice agents and multimodal systems are non-deterministic pipelines, but they are also
perception products. Quality depends on speech recognition, turn-taking, cameras, images,
documents, OCR, retrieval, model reasoning, final output, and the softer question of whether the
experience feels right to the user.

## 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

In a real release review, multimodal testing should include modality-specific error attribution,
audio quality slices, OCR accuracy, image grounding, accessibility checks, latency
distributions, human perception scoring, preference segmentation, and adversarial cross-modal
cases.