Könyv The AI Developer's Field Guide Tim O'Brien

The AI Developer's Field Guide

Volume I: Classes, Monsters, and Anti-Patterns in AI Coding

Szerző: Tim O'Brien
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Discursive
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
7 891 Ft
The AI Developer's Field GuideClasses, Monsters, and Anti-Patterns in AI CodingGenerative AI has cha...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2026
oldal
208
EAN
9798995259749
Enbook ID
52764688
Kiadó
Súly
371
Méretek
178 x 254 x 11

Teljes leírás

The AI Developer's Field Guide

Classes, Monsters, and Anti-Patterns in AI Coding

Generative AI has changed software development. It has also introduced a new class of failure.

Code can now look correct, pass tests, and still be wrong in ways that only show up later-in production, in edge cases, or in the hands of the next developer. This book is about those failures.

What This Book Covers
  • The most common AI coding anti-patterns in modern software teams

  • How tools like ChatGPT, GitHub Copilot, and Claude change code quality, review, and architecture

  • Why AI-generated code often produces plausible but incorrect results

  • How technical debt forms faster in AI-assisted development workflows

  • Practical techniques for code review, testing, and prompt discipline


A Practical Framework for AI-Assisted Development

This book introduces a working vocabulary for understanding how teams actually behave with AI:

  • Developer archetypes (Fighter, Wizard, Rogue, Cleric)

  • Failure modes (Scope Creep Kraken, Congealing Slop, Phantom Intern, and others)

  • Tool-driven patterns that emerge from real-world usage

These are not metaphors for their own sake. They are labels for repeatable problems teams encounter when using AI to write code.


Who This Is For
  • Software engineers using AI coding tools

  • Engineering managers and technical leads

  • Teams adopting AI-assisted software development

  • Anyone responsible for code quality, maintainability, and delivery


The Problem

AI makes it easy to generate code faster than you can understand it.

That creates predictable issues:

  • Systems that grow quickly but are difficult to maintain

  • Code that passes review but fails under real conditions

  • Teams that ship more while understanding less

Without shared language, these problems are hard to identify and harder to fix. This book provides that language.


What You Get
  • A clear way to diagnose problems in AI-generated code

  • Patterns you can reference in code reviews and retrospectives

  • Concrete practices that improve how teams use AI tools

  • A framework for staying effective as development continues to change

This book helps you avoid the most common mistakes-and build better habits before they become defaults.