---
name: tai-ch144-mlp-activations-by-decoder-layer
description: 'Apply chapter 144 of Testing AI, MLP Activations by Decoder Layer, 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 mlp activations by decoder layer.'
---

# MLP Activations by Decoder Layer

Skill name: `tai-ch144-mlp-activations-by-decoder-layer`

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

## Purpose

Concepts can spike, fade, persist, or reappear across decoder layers, giving testers another way
to compare model behavior.

## 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

MLP blocks inside decoder layers often carry feature transformations. In practical terms, some
concepts may activate early, some may persist across many layers, and some may reappear later
when the model prepares the answer.

## 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 production work, activation profiles can become regression tests. Store baseline profiles for
curated cases, compare distributions after fine-tunes or model swaps, and investigate large
movements even when output scores look flat.