---
name: tai-ch140-final-token-attention-across-decoder-layers
description: 'Apply chapter 140 of Testing AI, Final-Token Attention Across Decoder Layers, 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 final-token attention across decoder layers.'
---

# Final-Token Attention Across Decoder Layers

Skill name: `tai-ch140-final-token-attention-across-decoder-layers`

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

## Purpose

The final generated token often exposes what the model is using at the moment it chooses the
next word.

## 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

During generation, the model chooses each next token from the context available so far. Looking
at final-token attention across decoder layers shows what the model was focusing on while
preparing that next token.

## 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

When the system matters, final-token attention should be captured across layers and heads, then
compared with counterfactual prompts. If changing the source evidence does not change the
attention pattern or output, the system may not be grounded.