---
name: tai-theme-generated-code-and-model-internals
description: 'Use the Testing AI theme Generated Code and Model Internals to plan, review, or teach related AI quality work. Applies concepts and techniques from the book to testing AI, AI-generated software, and non-deterministic systems when relevant.'
---

# Generated Code and Model Internals

Skill name: `tai-theme-generated-code-and-model-internals`

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

## Theme Purpose

Use these approaches when testing AI-generated code, code-review evidence, generated tests, security and privacy risks, maintainability, and interpretability tools.

Apply these concepts when testing AI, AI-generated software, model-backed features, agents, search, chatbots, RAG systems, generated code, dynamic interfaces, or other software whose behavior can vary across runs, users, data, tools, or time.

## How To Use This Theme

- Identify the behavior, capability, risk, or release decision being evaluated.
- Choose the relevant concepts below and turn them into concrete eval cases, samples, traces, checks, rubrics, metrics, or release gates.
- Prefer evidence that supports a decision: ship, canary, hold, rollback, or collect more samples.
- Report by slices and severe failures when averages hide risk.
- Preserve enough evidence that another person or agent can understand what was tested, how it was measured, and why the recommendation follows.

## Concepts And Techniques To Apply

- Test AI-generated code for functional correctness, security, maintainability, dependency risk, hidden assumptions, and over-broad edits.
- Do not trust generated tests just because they pass; inspect coverage quality, assertions, edge cases, and oracle quality.
- Look for hallucinated APIs, fake packages, weak error handling, missing validation, unsafe defaults, and prompt-shaped code smells.
- Use code review, static analysis, runtime tests, security scans, and regression tests as validation layers.
- When useful, inspect model behavior with interpretability tools, concept visualization, attention analysis, activation steering, or model-editing experiments.

## Reporting Guidance

- State what was tested and what population the evidence represents.
- Explain uncertainty, missing coverage, severe failures, and known blind spots.
- Connect findings to a concrete decision or next action.
- Use topic-specific chapter skills only when deeper detail is needed; this theme skill should stand alone as practical guidance.