---
name: tai-ch128-visualizing-debugging-and-editing-llm-concepts
description: 'Apply chapter 128 of Testing AI, Visualizing, Debugging, and Editing LLM Concepts, 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 visualizing, debugging, and editing llm concepts.'
---

# Visualizing, Debugging, and Editing LLM Concepts

Skill name: `tai-ch128-visualizing-debugging-and-editing-llm-concepts`

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

## Purpose

Modern interpretability tools can reveal useful clues inside models, but they are instruments,
not magic explanations.

## 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

LLMs represent information across many layers of activations. Researchers and tool builders
increasingly use attention visualization, logit lens, activation patching, sparse autoencoders,
feature visualization, concept vectors, steering vectors, and model editing to understand why
models behave as they do.

## 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, combine interpretability with causal tests: activation patching, counterfactual
prompts, feature steering, and behavior evals before and after intervention. Model editing
should always be regression-tested broadly because changing one concept can move unrelated
behavior.