Interaction Acceptance Checklist: The Second List Before Launch
Are the states complete? Are dangerous actions reversible? Can the flow still be cut? Did you pick the right controls? Is the copy in plain language? A checkable interactive list — paired with the Taste and Psychology lists as one set
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is the key idea behind “Interaction Acceptance Checklist: The Second List Before Launch”?
Are the states complete? Are dangerous actions reversible? Can the flow still be cut? Did you pick the right controls? Is the copy in plain language? A checkable interactive list — paired with the Taste and Psychology lists as one set
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.
Taste acceptance can use screenshots; interaction acceptance needs hands-on clicks: throttle the network, empty the list, press the dangerous button, walk the flow end to end. The 14 items below are that action list, grouped in this chapter's nine-lesson order—each line can go straight to AI as a rejection reason. Check progress lives locally; bring a new product next time and keep going.
Source of the last item: About Face 4, Chapter 8 compares good software to a considerate person and lists a considerateness checklist—"good memory" is the cheapest, highest-yield line. Making users re-pick a city or retype an invoice title is software playing amnesiac.
The list is cold; the drill is hot. This cloud-notes app is built at real "AI one-shot" quality—five interaction accidents hit five lessons in this chapter. Use the checklist as a map; tap when you find one.
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That considerateness item expands into Chapter 8's full list: considerate software cares about preferences, answers asks, remembers, and doesn't dump its own problems on users. Three real scenes—judge considerate or rude.
All three series' lists are here: Taste for looks, Interaction for usefulness, Psychology for feel. Run them in order before launch—none substitutes for another.
Lessons 1–5: four handles: are the three states complete? Are dangerous actions reversible? Can the flow lose another step? Did anyone invent odd interactions? To judge usefulness, click in that order.
Interaction acceptance needs clicks: throttle the network, empty the list, press the dangerous button, walk the full path—screenshots won't catch interaction bugs.
Use the 14 items as rejection copy: send the failing line to AI verbatim—ten times faster than "optimize it more."
Three lists, one set: looks (Taste), works (this chapter), feels (Psychology)—run in order before launch.
Source: Original to Xiaoshan Academy's Interaction Engineering series; considerateness list from About Face 4, Chapter 8; other criteria cited per lesson (Alan Cooper et al.).
Turn the feeling in “How to catch interaction bugs at acceptance” into a judgment
“Taste acceptance can use screenshots;” 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 “Source of the last item: About Face 4 , Chapter 8 compares good software to a considerate person and lists a considerateness checklist—"good memory" is the cheapest, highest-yield…” 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 “Three lists, one set: looks (Taste), works (this chapter), feels (Psychology)—run in order before launch” 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.
From “How to catch interaction bugs at acceptance” to “Try it · Interaction acceptance checklist: walk all 14”
“How to catch interaction bugs at acceptance” grounds the problem in “Taste acceptance can use screenshots; interaction acceptance needs hands-on clicks: throttle the network, empty the list, press the dangerous button, walk the flow end to end. The 14 items below are that action…”. “Try it · Interaction acceptance checklist: walk all 14” then moves it toward “Source of the last item: About Face 4 , Chapter 8 compares good software to a considerate person and lists a considerateness checklist—"good memory" is the cheapest, highest-yield line. Making users re-pick a c…”. Together, they show that the lesson is not just a conclusion to remember, but a claim with conditions.
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?
- “How to catch interaction bugs at acceptance”: Taste acceptance can use screenshots; interaction acceptance needs hands-on clicks: throttle the network, empty the list, press the dangerous button, walk the flow end to end. The 14 items below are that action…
- “Try it · Interaction acceptance checklist: walk all 14”: Source of the last item: About Face 4 , Chapter 8 compares good software to a considerate person and lists a considerateness checklist—"good memory" is the cheapest, highest-yield line. Making users re-pick a c…
- “The closing point”: Lessons 8 – 9 : write it for AI : brief with persona + goal + scenario; the spec lists all five states, nails edge cases, spells out error prevention—together they're the full prompt
The final “The closing point” brings the discussion to “Lessons 8 – 9 : write it for AI : brief with persona + goal + scenario; the spec lists all five states, nails edge cases, spells out error prevention—together they're the full prompt”. The useful thing to carry forward is knowing which judgments must be revisited when input, scale, or risk changes.
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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