Most UI testing covers the happy path. This prompt flips the script and makes AI hunt for the three scenarios that wreck everything.
The technique comes from u/Significant-Strike40 on r/PromptEngineering. The author’s core insight is simple: stop asking AI to design your UI and start asking it to attack your UI.
The Prompt
Here’s exactly what the original poster shared:
“Describe a user flow for [App]. Now, identify 3 ‘Edge Cases’ (e.g., no internet, full storage, invalid input) and how the UI should handle them.”
Plug your app description into [App] and run it. That’s the whole setup. You don’t need a polished spec or a detailed product document. Even a two-sentence summary of what your app does is enough for the AI to map a working user flow and start poking holes in it. The more specific you are about the core action your users take, the sharper the edge cases will be. “A recipe app where users save and organize meals” gives better results than “a food app.” Give it a real job to do, not a category to guess from.
Why It Works
Most edge case thinking happens after a user complains. This prompt moves that conversation to before you build.
The structure is what makes it effective:
- Forces the AI to map the actual user flow first, not jump straight to guessing failure modes. This matters because the flow itself reveals the assumptions baked into your design. If the AI maps out “user logs in, adds item to cart, completes checkout,” you can already see three places where things can break before it even gets to step 2.
- Shifts it into attacker mode once it understands the flow. The transition is the key. You’re not asking for abstract failure modes. You’re asking it to look at the specific flow it just described and find exactly where that flow collapses under pressure.
- The inline examples (no internet, full storage, invalid input) seed the AI toward real-world breakage, not theoretical edge cases. Without those anchors, the AI tends to surface obvious, surface-level warnings. With them, it reaches for the kind of failures users actually run into on a Tuesday afternoon.
Because the AI just described the flow itself, the edge cases it surfaces connect to real states in your product. It’s not generating generic warnings. It’s pointing at specific moments where things fall apart. That’s the part most QA processes miss: not just knowing that failure modes exist, but knowing exactly when and where in the user journey they hit, and what the UI does when they do.
Variations Worth Trying
The base prompt is solid. Two ways to push it further:
- Scale up the count: “Identify 5 edge cases” gets you broader coverage without any extra setup. Going to 7 or more starts surfacing increasingly niche scenarios, which is useful if you’re building for high-stakes environments like healthcare, fintech, or anything where a broken state has real consequences beyond frustration.
- Add a user persona: “…from the perspective of a first-time mobile user on a slow connection.” Context shapes which failures actually matter. A returning power user and a first-time visitor on spotty wifi will break your app in completely different ways. Running the prompt twice with different personas is a cheap way to get coverage across your user spectrum without spinning up a full testing suite.
You can also chain both variations. Run the base prompt first to catch the obvious stuff, then re-run with a specific persona to stress-test the flows that matter most for your actual target user. Takes maybe fifteen minutes total and surfaces more useful input than most hour-long design reviews.
🛠 Use Cases
This works beyond apps. Same structure applies anywhere users interact with a flow:
- Onboarding sequences (user skips every optional step, never verifies email, lands in a state your flow didn’t account for because you assumed they’d complete setup linearly)
- Checkout flows (expired card, item goes out of stock mid-session, discount code applied after tax is calculated, address autofill drops the apartment number)
- Form-heavy interfaces (required field left blank, copy-paste from a spreadsheet breaks character limits, user tries to submit twice because the loading state isn’t visible, browser autofill drops incompatible data into a custom field type)
- Mobile-first products (low battery mode active, background refresh disabled, user switches apps mid-flow and returns to a stale session, phone rings while they’re mid-checkout)
If your product has a flow, it has edge cases. Better to find them here than in a support ticket. The difference between teams that ship stable products and teams that spend weekends firefighting is usually not testing discipline. It’s where in the timeline they start asking “what could go wrong.”
Try It on Your Next Build
The original discussion is in r/PromptEngineering if you want to see what others are doing with it. Worth a look before your next UI sprint. Copy the prompt, drop in your app description, and give the AI ten minutes to break your product before your users do.
The ‘Edge-Case’ Stress Test for UI.
by u/Significant-Strike40 in PromptEngineering