Phase 0: The AI Code Quality Crisis · 50 min · Python · pytest

Why AI Code Fails Differently

AI code is not human code with a few typos. It fails in patterns.

Real-world application: Engineers who can articulate specific AI code defect patterns -- not just 'AI makes mistakes' but the exact categories (variable assignment, repetitive logic, hallucinated APIs, missing exception handling) -- demonstrate they have studied the research and will approach AI code review with the right skepticism level.

What you will learn

Identify the four primary AI code defect categories: variable assignment errors, repetitive logic, hallucinated APIs, and missing exception handling
Explain the 1.7x defect multiplier and why AI code is less structurally complex but more defect-prone
Recognize AI-generated defect patterns in real code samples
Distinguish AI code defects from traditional human code defects

Introduction

Why AI Code Fails Differently

Objective

Run the defect pattern analyzer on a provided AI-generated codebase and produce a categorized defect report with severity rankings.

AI code is not human code with a few typos. It fails in patterns.

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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