Reviewing AI-Written Code: Catching What the Machine Got Wrong

AI assistants can produce code at astonishing speed — but speed is not correctness. Every generated function, every suggested fix, every refactored block carries hidden assumptions, hallucinated APIs, and logic gaps that only an experienced reviewer can catch. This book teaches you to read machine-generated code the way a proofreader reads a manuscript: not trusting the surface, but looking for the subtle failures of understanding that compound into production bugs. You will learn to identify the patterns of confident wrongness unique to AI output — imaginary libraries, off-by-one errors that look right, security blind spots that slip through conventional review. Through practical checklists, worked examples, and a structured mindset, you will build a repeatable process for verifying AI code before it reaches your codebase. Whether you are a senior engineer asked to approve AI pull requests or a team lead establishing review guidelines, this book gives you the tools to own every line — even the ones you did not write. Part of The AI Maker Library.

About the author

Sophia Lund Alvarez writes for The AI Maker Library.

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