AI Prompt Generator for App Store Optimization
Fill in the blanks and copy — 7 staged AI prompts for repositioning an app around a higher-traffic keyword and making the listing stand out, based on a real app-revival that moved from #10 to #2 for its target term.
Fill in the blanks below — the prompt updates live. These work with any AI chat tool (Claude, ChatGPT, Gemini, etc.); nothing here is tied to one specific model.
How it works
These prompts come from a documented app revival: an old notification-history Android app that was rebranded around the higher-traffic phrase "reveal deleted messages", given a contrasting red icon in a field of blue and green ones, and rewritten to match — moving from roughly #10 to #2 for the target term in the US store and #1 in the Philippines.
Run them in order for a repositioning. Prompt 1 finds the keyword worth chasing and checks the app can honestly own it. Prompt 2 breaks down what the ranking competitors look like so you can see the gap. Prompt 3 forces out one genuinely remarkable difference rather than a longer feature list. Prompts 4 and 5 turn that into an icon and screenshot brief and a rewritten listing. Prompt 6 sets up the ratings and social-to-store loops within store policy, and Prompt 7 makes you test one change at a time so you can tell signal from noise.
Every prompt is told to flag over-promising and policy risk. Standing out is not the same as misleading: a claim or screenshot the app does not back up gets reported and can get the listing pulled.
For pricing the app by country, see the regional pricing calculator. For building the demand before the launch, see the demand-first product prompts and the Reels strategy prompt generator.
FAQ
What is app store optimization (ASO)?
ASO is the practice of improving an app's store listing — its title, subtitle, description, keywords, icon, screenshots, and ratings — so it ranks higher for the searches people actually type and converts more of the people who see it into installs. It is the app-store equivalent of SEO, and on both the App Store and Google Play, search is the largest source of new installs for most apps.
Should I reposition my app around a higher-volume keyword?
Only if the app honestly delivers what someone searching that term wants. Chasing a bigger keyword the app does not fit brings installs that uninstall within days, which lowers your ranking and can trigger review backlash or a policy strike. Prompt 1 is built to pressure-test that fit before you commit, and Prompt 5 keeps the rewritten listing from over-promising.
What is the Purple Cow idea and how does it apply to an app icon?
"Purple Cow", from Seth Godin, is the idea that in a crowded market only a remarkable product gets noticed and talked about — a brown cow is invisible, a purple one is not. For an app listing it means one deliberate, describable difference: if every competitor icon is blue or green, a red one stands out on the results page; if every listing makes the same promise, a sharper or more specific one gets the tap. It has to be a real difference, not a misleading one.
Do these prompts work with ChatGPT, Claude, and Gemini?
Any AI chat tool. They are structured analysis and copywriting briefs, not model-specific. The AI cannot see live store data, so give it what you can observe — competitor names, what their listings look like, your rough install and ranking numbers — and treat keyword-volume estimates as directional.
How long does an ASO change take to show results?
Days to a few weeks, and unpredictably. Rankings shift with your install velocity, retention, ratings, and what competitors do, so a term you reach #2 for can slip back. Prompt 7 sets up a two-week, one-change-at-a-time test so you can tell whether a change actually moved anything rather than guessing from noise.
Can I offer users a reward for leaving a 5-star review?
No. Both Apple and Google prohibit incentivised, fake, or manipulated reviews, and it can get an app removed. What is allowed is asking at the right moment — after a real win in the app, not on launch — and routing unhappy users to a feedback form instead of the public rating. Prompt 6 covers both loops within policy.