TL;DR
Feed an AI model your audience, top three benefits, and tone, then ask for variants that already respect the character caps. A model drafts a full iOS + Android listing in about 20 minutes. Spend the saved time where conversion actually moves: the subtitle and the first two description lines. One hard fact to internalize before you start: Apple’s built-in A/B test (Product Page Optimization) only tests images (icon, screenshots, previews), not text. To compare copy, you ship a metadata-only update and measure before/after. (All facts current as of June 2026.)
The six surfaces that decide a download
You are submitting a new app or refreshing a listing. A handful of fields carry almost all the conversion weight, and each one has a hard character cap that the model needs to know up front. Writing them by hand burns half a day on fiddly counting.
The two caps that matter most are tiny: the iOS subtitle and the Google Play short description are the lines a browsing user actually reads before deciding to scroll. Everything else is for the curious tap-through and for keyword surface.
Character limits, per locale (June 2026)
Every field below is counted per language locale — a French or Japanese listing gets its own copy of each field and must fit the same cap. Apple counts each character as one, including spaces, punctuation, and CJK or emoji glyphs.
| Field | Apple App Store | Google Play | Where it shows |
|---|---|---|---|
| App name / title | 30 chars | 30 chars | Search results, listing top |
| Subtitle (iOS) | 30 chars | — | Under the name in search + listing |
| Short description | — | 80 chars | Top of the Play listing, above the fold |
| Promotional text (iOS) | 170 chars | — | Above the description; editable anytime, no review |
| Description | 4,000 chars | 4,000 chars | Only first 2-3 lines show before “more” |
| Keywords field (iOS) | 100 chars | — | Hidden; comma-separated, no spaces |
Google Play has no separate hidden keyword field: it indexes the title, short description, and full description, so keywords have to read naturally inside the prose.
When AI is the right tool here
Reach for a model when you already know who the app is for and what its top three features deliver. Models are strong at three jobs: compressing benefit language to fit a 30-character subtitle, expanding and de-duplicating a 100-character keyword field, and producing five subtitle variants so you have something to test.
It also handles localization well. Keep one clean English source, then generate per-market variants with a short cultural-nuance note for each. Always send those to a native-fluent reviewer before shipping — machine translation alone misses idiom and breaks character counts in CJK markets.
When not to rely on AI alone
A model does not know your category’s current ASO ranking signals; those shift every quarter and no model has live search-volume data. Pair every AI draft with a manual look at three competitors that currently rank for your target keywords, and validate keyword candidates in a real ASO tool (App Radar, AppTweak, Sensor Tower) before committing the 100-character field.
For regulated categories (finance, health, kids), route the keyword field and description through a compliance review. Apple and Google both reject listings that imply unverified medical or financial claims.
What to feed the model
- App name plus a one-line tagline
- Top three features, each written as a user benefit, not a technical name
- A specific audience (“freelance designers tracking invoices,” not “small businesses”)
- Five competitors and one thing each does well
- Two tone adjectives (for example “calm + capable,” not “innovative”)
Copy-ready prompt
Replace each [bracketed] placeholder with your own input before sending.
You are an App Store optimization writer. Produce a full listing for both
Apple App Store and Google Play.
App: [name], [tagline]
Top 3 features (feature -> benefit):
- [feature_1]: [benefit_1]
- [feature_2]: [benefit_2]
- [feature_3]: [benefit_3]
Audience: [specific_audience]
Competitors I respect: [comp_list]
Tone: [two_adjectives]
Output, each labeled with a live character count:
1. iOS subtitle: 5 variants, each at or under 30 characters, leading with a benefit verb.
2. Google Play short description: 3 variants, each at or under 80 characters.
3. iOS promotional text: 3 variants, each at or under 170 characters.
4. Description (works for both stores):
- First 2 lines: punchy, benefit-first, no "Welcome to..."
- 3 short paragraphs, one per feature
- a 4-6 item bullet list of secondary features
- one-line closing CTA
5. iOS keywords field: 100 characters, comma-separated, no spaces, no words
already used in the app name.
Rules: no superlatives without proof, no "revolutionary" or "next-generation,"
never list a feature without its benefit.After the model replies, paste the subtitle and short-description variants back and ask it to recount each in characters — models routinely miscount the first time.
How to check the output before you ship
- Recount the caps by hand. Paste each subtitle and short-description variant into a character counter. A line that “looks short” is often 32 characters.
- Pair every feature with a benefit. “Cloud sync” is a feature; “Pick up where you left off on any device” is the benefit a buyer wants.
- Read the first two lines on a phone. That is the only copy most users see before tapping “more.” If the value is not obvious there, rewrite.
- Sanity-check keywords against the live store. Search your top three terms in the App Store and confirm at least one top-10 competitor uses similar phrasing.
Testing your copy: what Apple and Google actually allow
This is where most guides get it wrong. Apple’s Product Page Optimization (PPO) runs a real split test — up to three treatments against your live page for as long as 90 days — but it can only change visual assets: app icon, screenshots, and preview videos. It cannot test a subtitle, description, or keyword change.
To compare two versions of your copy on iOS, you submit a metadata-only version: a listing-text update that ships without a new build. Change one field, let it run two to four weeks, then compare conversion to the prior period. That is sequential testing, not a true A/B test, so hold everything else steady while it runs.
Google Play does let you A/B test text. Store listing experiments split live traffic across up to three variants and include the short description, so Android is the place to A/B test copy directly. Apple raised custom product pages to 70 per app in early 2026, and Google’s custom store listings (up to 50) now surface in organic search — both let you point different keyword cohorts at differently worded pages.
Test one element at a time, subtitle first. Once a winner emerges, re-prompt the model with that subtitle so the rest of the listing aligns to it.
Common mistakes
- Features without benefits (“Cloud sync” instead of “Pick up on any device”).
- Keyword stuffing the description — Apple ignores it for ranking and users read it as spam.
- Subtitles that are slogans (“The future of notes” tells a buyer nothing).
- Shipping one description to both stores — Google indexes description keywords and has an 80-char short description; iOS hides keywords and uses a 30-char subtitle. The body can overlap, but the headline fields differ.
- Skipping screenshot captions, which are often read more than the description itself. See app store screenshot copy with AI.
FAQ
How long should the description actually be? Front-load it. The first two or three lines plus the bullets carry conversion; the rest of the 4,000 characters serves curious tap-throughs and, on Google Play, keyword indexing.
Can I A/B test my subtitle on the App Store? Not with PPO — that tests images only. For iOS copy, ship a metadata-only version and compare conversion before and after. On Google Play you can split-test the short description directly with store listing experiments.
Does AI help with the iOS keyword field? Yes for expanding and de-duplicating candidates within the 100-character cap, but validate actual search volume in a dedicated ASO tool before you lock it in.
Which model should I use? Any current frontier model handles this well. As of June 2026, GPT-5.5, Claude Sonnet 4.6, and Gemini 3.1 Pro all produce solid listing drafts; Claude tends to hold character caps more reliably, but recount by hand regardless.
How do I handle localization? Localize per top-five market and have a native-fluent reviewer check each one. Machine translation breaks CJK character counts and misses idiom.
Related
For Apple-specific patterns, see app store listing prompts; tune the keyword field with App Store keyword research using AI; boost on-page conversion with app store screenshot copy with AI; and align the strategic story using product positioning prompts.