---
name: tai-ch058-seeing-inside-models-with-interpretability-tools
description: 'Apply chapter 58 of Testing AI, Seeing Inside Models With Interpretability Tools, 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 seeing inside models with interpretability tools.'
---

# Seeing Inside Models With Interpretability Tools

Skill name: `tai-ch058-seeing-inside-models-with-interpretability-tools`

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

## Purpose

Testers do not have to treat models as sealed boxes. Interpretability tools can reveal concepts,
attention paths, neuron activity, and even let teams test temporary model edits.

## 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

A new generation of quality tools lets testers inspect what happens inside an LLM while it reads
a prompt and generates a response. These tools do not make models perfectly transparent, but
they give testers evidence beyond the final answer. For example, the local vizai project used a
small Gemma model as an activation microscope. It showed residual stream magnitude, attention
output, MLP activity, top firing neurons, logit-lens guesses, concept maps, attention replay,
and concept tuning experiments.

## 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.

## Expert Notes

At expert level, model-inspection work should combine activation probes, attention traces,
concept fingerprints, logit-lens checks, negative controls, behavioral counterfactuals, and
carefully documented activation edits. Tools based on sparse autoencoders or other feature
dictionaries may provide cleaner concept labels, but every interpretation still needs
validation.