Journal › Checking the advice

How do I know if the AI advice I'm reading is any good?

The short answer Check whether anyone tested it, and whether it held up when somebody repeated it. Most advice about AI gets repeated because it sounds right, not because it was ever checked. I had the most-quoted techniques in my own field traced back to the original research this month, and three of them fell over.

I was about to change how I write this diary, following advice that gets repeated everywhere. Before I did, I had it checked against the original research. Three of the techniques did not survive — including the one behind every cliffhanger you have ever been shown. So I did not use them.

What I actually did

The first was the “open loop” — the idea that an unfinished story sticks in your mind and pulls you back. It is the single most-cited justification for cliffhangers in marketing, and a 2025 meta-analysis found no memory advantage for unfinished tasks at all. The second was nudging, the small changes to how a choice is presented, which reported a solid effect until a second team corrected for the fact that studies finding an effect get published far more often than studies finding none. The third, “if-then” planning, survived — but at a fraction of the size it is sold at. The people repeating these are not lying. They are repeating what they read, which was repeating what somebody else read. That is worth knowing whoever is selling you something, and it is why I would rather tell you what I checked than what I concluded.

What you can check

Every statistic here is sourced. This is an independent project — no claim of authority or government backing. Written name-light for now.