Who it is for
For anyone who leans on AI at work and wants to spot, survive, and recover from its most common failure modes instead of getting burned by them.
AI Knowledge Series / beginner
What Could Go Wrong?
AI fails in predictable patterns. Learn the most common failure modes so you can spot, survive, and recover from AI errors in your work.
The problem
It starts with the situation you're actually in.
You ask an AI assistant for the population of a mid-sized city, a quick figure for a report. It answers instantly, with a clean number and a confident sentence. This book is about that gap, the space between sounding right and being right.
Tomorrow you'll open a chat window, type a question, and get back a paragraph that sounds right. What changes is you, and how you read it.
Outcomes
What you'll be able to do.
What "Hallucination" Actually Means
Inc., a 2023 federal matter in New York, a lawyer submitted a brief citing six cases. You'll see what went wrong and why it looked fine.
The Résumé Filter That Learned the Wrong Lesson
A hiring tool was trained on ten years of a company's résumés to predict who gets an interview. The data was real, and that was the problem.
A Wrong Answer You Already Sent
You ask an AI to summarize a contract clause and it tells you the agreement auto-renews unless you cancel 30 days out. You forward that to a colleague before you check it.
The AI Optimizes Your Words, Not Your Goal
You hand a document to an AI and ask it to "summarize the key points." It looks fine, which is exactly how it slips past you.
Inside the book
A closer look at the work inside.
The Day the Answer Looked Perfect checklist
- Another chapter digs into hallucination, the invented facts, fake citations, and broken links from the examples above, and gives you a checklist for catching them.
- Another chapter covers bias, the skewed and one-sided output that comes from the data these tools learned on.
- Another chapter looks at misunderstanding, what happens when the AI follows your words but misses your intent, or gets misled by hidden text in the files and sites it reads.
- Another chapter pulls it all into one repeatable workflow: spot the likely error, survive it, recover from it.
- Another chapter turns the whole thing into a steady working mindset you can keep using long after the last page.
Three Quiet Failures You'll Actually Meet
These aren't rare edge cases, they're everyday situations. Take the summary that drops the exception: you feed the AI a four-page policy and ask for the key points, and it returns a tidy, well-written list that quietly leaves out the one clause saying the policy doesn't apply to contractors, the exact detail you needed.
Practice
Take an AI answer you've received recently, or generate one now on a topic you know well, and run it through the checklist. Tag each claim: [VERIFIED] checked against a real source, [UNCHECKED] plausible but unconfirmed, [SUSPECT] shows two or more hallucination signals.
What "Hallucination" Actually Means checklist
- Invented facts. Dates, statistics, quotes, definitions, or events that sound right but aren't. "The technique was first published in 2017" when it was 2014, or a population figure.
- Fake citations and sources. Books, studies, articles, or legal cases with realistic titles and authors that were never written.
- Broken or wrong links. URLs that 404, or that lead somewhere unrelated. The model assembles a plausible-looking web address character by character; it doesn't visit the page unless.
- Confident misattribution. A real quote assigned to the wrong person, or a real study summarized with conclusions it never reached.
- Made-up specifics. Product features that don't exist, menu options in software that were never there, API functions a developer can't find in the documentation.
A Leading Question at Work
Say you prompt: list three peer-reviewed studies showing standing desks improve productivity, with author names and publication years. The request assumes three such studies exist and demands names and years. You've combined an obscure-specificity request with a leading frame.
What Could Go Wrong? frameworkInside the book
Visual preview
A diagram you can keep open while you work.
Table of contents
6 chapters, built to be read in order.
01
Why AI Fails Differently
02
Confident and Wrong — The Hallucination Problem
03
Garbage In, Bias Out
04
When the AI Misunderstands You
05
Your Safety Net — Spot, Survive, Recover
06
Working With AI, Eyes Open
134
Pages
8,663
Words
29
Exercises, checklists & tools
6
Chapters
Formats
Three formats. One purchase.
EPUB, PDF, and HTML are included so the book can work on an e-reader, as a designed copy, or as a searchable desk reference.
EPUB
For e-readers and reading apps.
The designed edition with diagrams and layouts intact.
HTML
Searchable, copy-pasteable, and practical as a reference.
Complete guide
What Could Go Wrong?
$4.99
beginner guide pricing
- ✓EPUB + PDF + HTML included in one purchase
- ✓6 chapters from the complete guide
- ✓DRM-free files for your own devices
- ✓7-day refund review for duplicate purchases, access issues, wrong files, or materially defective downloads
Secure checkout / instant download / tax handled at checkout
Before you buy
Questions, answered.
Does this include the full book?
Yes. You get the complete edition, including the chapter sequence and internal materials described on this page.
Which formats are included?
EPUB, PDF, and HTML are included so you can read on an e-reader, keep a designed copy, or use the searchable browser version.
What is the refund policy?
Because this is an instant digital download, broad change-of-mind refunds are not offered after the files have been accessed. Refund requests are reviewed within 7 days for duplicate purchases, accidental purchases before access, access failures we cannot fix, wrong files, corrupted files, or pages that materially misdescribe the book.