Skip to content
Decision Process

Where I actually use this

The two-question check works on any decision that matters. Here's where I push it hardest — and where I wrote down what AI got wrong as well as right.

// TL;DR
Problem
a check you only half-trust is a check you'll skip.
Fix
see it run on the highest-stakes decision I make, where getting it wrong has a real bill attached.
Payoff
proof the method earns its place before you lean on it.

If you’ve read the others, you have the whole check: make the AI name the exact document it’s looking at, make it hand you one fact you can verify in under a minute, and bin the lot if it fails either. It works on a phone contract, a letter from a doctor, an email from your child’s school. Nothing in it is about money.

So why do I keep going on about it? Because at some point I had to test it somewhere a wrong answer comes with a bill.

The version with a bill attached

The decisions where this matters most all share one thing: getting them wrong comes with a bill. Sometimes the bill is money — a fund you’re choosing, a fee you didn’t spot. Sometimes it’s a letter from a doctor you’re about to act on, a contract you’re about to sign, or the paperwork for a trip you’ve already booked. Misread a holiday email and you lose an afternoon. Misread one of these and you lose something that stings — and the AI sounds every bit as helpful walking you off the edge as it does when it’s right.

Here’s one that caught me, and it had nothing to do with money. I asked an AI to help sort the paperwork for a trip to the States, and it told me — with total confidence — that I needed something called a “Sofi.” There’s no such thing. The document was an ESTA. It hadn’t checked anything; it had produced a plausible-sounding word and handed it over as fact. Thirty seconds of verifying caught it. Get that one wrong and you’re turned away at the airport on a trip you’ve already paid for.

So I run the same two questions on anything that carries a bill, write down the verdict, and — the part most people skip — write down the times the verdict was wrong. The place I’ve pushed it hardest, and logged the most catches, happens to be money: it’s an unforgiving proving ground, and a confidently wrong AI makes a far more useful witness than one that simply agrees with me. It’s less flattering to publish, which is presumably why nobody does.

Here’s the check in a single paste, whatever the stakes are for you — keep it handy for the next answer that matters:

// Paste this before you act on an AI answer

Before you answer anything else: name exactly what you’re looking at — the specific document, page, letter or account. Then give me one fact about it I can check myself in under a minute. If you’re not certain what you’re looking at, say so before you go on.

Three places to see it work

  • The whole method, laid out — the four questions in full, with prompts you can copy.
  • Where AI got it wrong — a running log of the answers that didn’t survive a check. The point of the site, really.
  • One that fooled me for a paragraph — I asked an AI to review my website and it confidently reviewed someone else’s, then praised a framework that wasn’t mine. Every fact in it was true. None of it was about me. That is the failure the check is built to catch — screenshots and all.

Start wherever the stakes are highest for you. Mine happen to involve money. Yours might be a contract, a diagnosis, or an email you’re about to reply to at eleven at night. The check doesn’t care which. It just asks the AI to prove it’s looking at your problem before you believe a word — because how often AI is quietly wrong on things you can verify is the whole reason the check exists.

Ben Dixon
// Written by Ben Dixon

Ben tests ways of getting reliable answers from AI on his own investing — documenting what each model got wrong, what each one caught, and the prompts that survived the cuts. About Ben →

// Keep reading
Decision Process

How to tell if an AI answer is true in 30 seconds

Two questions — and a prompt you can copy — that catch a confident, wrong AI answer before you act on it. No jargon, no AI knowledge needed.

Decision Process

Make it show its working

An AI hallucination is a model mixing what it knows with what it's making up, in the same smooth tone. One prompt makes it sort the two into separate lists — so you can see the invented parts.

Decision Process

The whole method, in four questions

The four questions I run any AI answer through before I trust it — shown end to end on one everyday example, with a prompt you can copy. No jargon.

// New here?

The site runs AI on real investing decisions. Start with the Prompt Stack for the four-stage framework, or the Field Guide PDF for the condensed version — free, no email.

← All posts More in Decision Process →