AI Product Psychology · 40 Soul-Searching Questions
Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness / algorithm aversion / label discount / emotional attachment / payment & pricing / silence bias / how to write experience metrics into OKRs
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is the key idea behind “AI Product Psychology · 40 Soul-Searching Questions”?
Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness / algorithm aversion / label discount / emotional attachment / payment & pricing / silence bias / how to write experience metrics into OKRs
Turn taste into a behavior the product can repeat. The useful outcome is not a nice opinion. It is a visible rule, a small example, and a way to tell when the experience falls below the bar.
Capture one before-and-after example that shows the quality bar without extra explanation.
Polish that improves the surface while leaving the user's uncertainty untouched.
Turn the feeling in “AI Product Psychology · 40 Soul-Searching Questions” into a judgment
“Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness /…” points out that AI has lowered the bar for making something usable. The skill readers need is noticing what is wrong and turning that feeling into an actionable requirement.
Watch the user's next action, not just the surface
Turn “Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness /…” into observable questions: does the user know what happened, what to do next, and how to recover from an empty or failed state? Does the hierarchy make the important information visible first?
Pretty is not the same as usable
Apply “Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness /…” to a second screen or flow. Record one moment of hesitation and the user action after the change; observable behavior is stronger evidence than polish alone.
Take the example one step further
The page first makes this point: “Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness /…”. Turn it into a small exercise rather than a sentence to memorize: write down the input, expected result, and the observation that would make you re-check the judgment.
Carry the judgment into the next situation
For experience work, turn abstract impressions into user actions: did the person understand the state, find the next step, recover from an error, and want to continue?
- “AI Product Psychology · 40 Soul-Searching Questions”: Each question comes with what they’re assessing, an answer framework, and bonus points: perceived performance / peak-end tradeoffs / trust calibration / dismantling defensiveness /…
Finish with a small, reversible exercise: put the page's judgment into a real input, write the expected result, and name the signal that would make you stop and verify it.
I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.
After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.
When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.
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