How to Write App Store Descriptions with AI (2026): ASO Copy That Drives Downloads

Last updated: October 2026

Most app store description optimization advice still treats the App Store and Google Play as one channel with two logos. They are not. Apple does not index your description at all, while Google reads every word of it. That single difference should change what you write for each store, and it is exactly the kind of rule-bound, repetitive writing that AI handles well when you brief it properly. This guide gives you the verified 2026 field limits for both stores, a prompt chain that turns a raw feature list into compliant listing copy, a worked example, and the compliance pass that keeps you out of rejection territory.

App store description optimization with AI works like this: on Google Play you write search copy for a 4,000-character field that is indexed, targeting roughly 2.5-3% keyword density. On the App Store you write pure conversion copy, because Apple indexes only your name, subtitle, keyword field, and category. Feed each store's rules to your AI tool, then audit the output before submitting.

The character budget: every text field on both stores, verified

Before writing a word, know the exact space you are filling. These limits were read directly from Apple's developer documentation and Google Play Console Help pages in October 2026.

Two details on this table save people every week. First, Apple's promotional text is the only major listing field you can update without shipping a new version, so treat it as your standing announcement slot. Second, Google Play's 80-character short description sits above the fold on nearly every device and carries heavy indexing weight, so it deserves as much drafting effort as the full description. If you want a deeper treatment of that field pair, our guide to app release notes covers the promotional text escape hatch in detail.

Apple ignores your description for ranking. Google indexes every word

This is the split that most listicles gloss over, and it should drive your entire workflow.

On the App Store, Apple's search documentation is explicit: ranking comes from text relevance across your title, subtitle, keyword field, and primary category, plus user behavior such as downloads, ratings, and reviews. The description is not a ranking input. Even the promotional text "doesn't affect your app's search ranking" according to Apple. So your Apple description has one job: convert the person who already found your page. Lead with the single biggest benefit, because only the first sentence or two is visible before the "more" tap.

On Google Play, the full description is indexed text. The working density range cited across ASO tooling is 2.5-3% for a target keyword, and Google supports 77 available locales for listing localization. The same description that reads like persuasive copy on Apple should read like well-structured, keyword-rich long copy on Play, with your primary terms woven into the hook, the benefit paragraphs, and the feature list.

The practical rule: write two descriptions, not one description translated between consoles. Apple gets conversion copy. Play gets search copy that still reads naturally.

How to write your store description with AI, step by step

Here is the full workflow, start to finish. It takes an evening once you have your keyword research done.

1. Collect your inputs. Gather your raw feature list, your three to five target keywords per store, your brand tone in two sentences, and any proof points you are allowed to state (ratings, download counts, certifications). AI cannot invent proof for you, and you should not let it try.
2. Write the Play full description first. Use a structured prompt: "You are an ASO copywriter. Turn this raw feature list into a Google Play full description of at most 4,000 characters. Structure: a hook sentence stating the app's category and core benefit, three benefit-led paragraphs grouping the features, a bulleted feature list with keywords woven in naturally, a short trust paragraph, and a closing call to action. Weave these target keywords at 2.5-3% total density: YOUR KEYWORDS. Avoid all price and deal mentions, ranking claims such as number one or best, emoji, all-caps words, and competitor names. Keep sentences under 20 words. Output the character count at the end."
3. Write the Apple description second, separately. Prompt: "Write an App Store description of at most 4,000 characters for APP NAME. Apple does not index the description, so optimize purely for conversion. The first sentence must state the single biggest benefit because it appears before the more tap. No keyword stuffing, no prices since the page already shows them, plain text with line breaks only. Tone: YOUR TONE. End with three short why-choose-us lines." For more on writing benefit-first interface copy, see our piece on UX microcopy with AI, which applies the same discipline to screens.
4. Sprint the short fields. Ask for ten Play short descriptions of at most 80 characters and ten Apple subtitles of at most 30 characters, each containing one primary keyword and none of the banned words. Rank them by clarity, then pick manually. Never auto-publish the first output; the short fields are too small to waste on a mediocre line.
5. Run the compliance audit. Paste the drafts back with: "Act as an app reviewer. Audit this listing text against Apple's keyword rules and Google Play's metadata policy. Flag every violation with the exact phrase, explain why, and rewrite each flagged phrase compliantly while keeping the persuasion." This single step catches the stuffing, banned claims, and emoji that trigger rejections.
6. Localize per storefront, not word for word. For each additional language, brief the AI to adapt rather than translate: local examples, local social proof, consistent digit style, and your localized keyword list at natural density. Play lets you manage 77 locales; Apple supports its own per-language listings. Verify each translation with a native speaker before it ships.
7. Measure, then iterate on the next version. Track listing conversion rate in App Store Connect and Play Console, not impressions. On Play you can also test description variants with store listing experiments. Apple description changes require a new binary, so batch your copy tests with your release cadence. If you are also drafting the update text itself, the release-notes guide linked above covers that field.

From feature list to finished listing: a worked example

Say you run TaskFlow, a fictitious SaaS task manager for small agencies, and your raw input is a bullet dump: kanban, time tracking, client portals, invoicing, Slack sync, mobile apps. Your Play keyword list: task management app, team task planner, client project tracker, invoicing for agencies.

The weak Play short description a founder would write alone: "TaskFlow is a task management application with many features for teams and businesses." That is 86 characters, keyword-thin, benefit-free, and over the 80-character limit.

The AI-assisted rewrite: "Run client projects end to end: tasks, timers, and invoices in one app." Seventy-two characters, one primary keyword, one concrete promise, zero banned words.

The weak Apple first line: "TaskFlow is a project management tool designed to help teams organize their work." That names the category and stops.

The conversion-first rewrite: "Bill every hour your team actually works." Seven words, a benefit an agency owner feels, and the kind of line that survives the "more" tap because readers want the second sentence.

For the Play full description, the prompt in step 2 produces a 1,400-character draft: hook, three benefit paragraphs (delivery, billing, client communication), a bullet list carrying the remaining keywords, a trust line on ratings and data security, and a closing call to action. You then hand-trim repetitions and verify the density is inside 2.5-3% for the primary term. Total human time on the whole listing: about two hours, most of it judgment calls the AI should not make alone.

Writing both store listings this way is exactly the kind of structured multilingual drafting an AI writing platform does well. ArWriter (from $4.99/month) runs this prompt chain across its 37-plus tools with bilingual output, so you can generate the English and localized variants in one workspace instead of juggling tabs: https://app.arwriterai.com/

The compliance pass: keep AI copy out of rejection territory

Generative copy fails store review in predictable ways. Run this checklist against every draft before it touches a console.

• No ranking or superiority claims anywhere in Play metadata: no "best," no "number 1," no award language you cannot substantiate on the spot.
• No price, discount, or deal language in Play titles and short descriptions. Google names these directly in its metadata policy.
• No calls to action and no emoji in the Play short description. Both are listed prohibitions, not style opinions.
• No keyword stuffing on either store. Apple warns that unrelated or repeated keywords in the description trigger issues; Google's keyword spam policy applies to every translation of your listing, not just the default language.
• No unattributed testimonials or quotes from reviews you cannot source.
• No graphic element may smuggle text: Play caps graphic alt text at 140 characters and applies the same content rules to it.

The reliable pattern is to paste those six bullets into your audit prompt so the model checks against the real policies rather than its memory of them, which drifts. AI is confident about store rules it half remembers; your job is to hand it the current text.

Custom store listings and localization: one app, fifty storefronts

Google Play lets you create up to 50 custom store listings per app, targeted by country, by keyword, and by audience segments including churned installers, lapsed users, and past buyers. Play Console now also generates custom listing descriptions with Gemini directly inside the console. That means your English default listing is just the base layer; your UK listing can reference local integrations, your German listing can lead with data privacy, and your listing aimed at churned users can lead with what changed since they left.

Apple's parallel move is quieter but bigger for discovery: Apple now generates app tags from your metadata using large language models and surfaces them in search. Sloppy, thin, or mismatched metadata produces vague tags. Coherent metadata that consistently names your category, audience, and use cases produces sharp ones.

Localization is where most teams leave installs on the table. Working app store localization keywords into naturally written per-language listings routinely beats machine-translated defaults, and the same custom-listing machinery lets you test a market before committing to a full translation. Pair the listing push with a solid onboarding email sequence once installs arrive, because the funnel does not end at the install button.

How a two-person team in Berlin rebuilt a flat listing

Jonas Weber runs Klar, a habit-tracking app, with one other person from a co-working space in Berlin. For most of 2025 their Play listing read like release documentation: "Klar helps you track habits with reminders and statistics." Impressions were steady at around 40,000 per month, but listing conversion sat at 9.4%, and installs had been flat for two quarters.

They spent a single evening on the workflow above. The Play full description went from 380 characters of feature listing to a 1,600-character structure with a benefit hook, three grouped benefit paragraphs, and a keyword density inside the working range. The short description was rewritten four times until it fit 80 characters with the primary keyword intact. On Apple, where they had duplicated the Play text, they wrote a separate conversion-first description and rebuilt the subtitle around their strongest benefit.

Six weeks later, impressions were essentially unchanged, but Play listing conversion had moved from 9.4% to 13.1%, which on the same traffic meant roughly 40% more installs from the identical audience. Their App Store page showed a smaller but real lift. The lesson Jonas repeats to other indie founders: the store was already sending them traffic; the copy was the leak.

Writing for AI discovery: app tags, Ask Play, and what changes

Store search is becoming store recommendation. Apple generates app tags from your metadata using large language models, and those tags influence how your app surfaces in search. Google Play's Ask Play answers user questions with AI-driven app recommendations drawn from listing content. Both shifts reward the same thing: descriptions written in clear, complete, benefit-led sentences that a language model can parse, not fragmented keyword strings.

Concretely, three habits future-proof your listing. Write full sentences that name the job to be done, the audience, and the outcome. Keep claims consistent across your title, subtitle, description, and screenshots, because models cross-check your fields against each other. And keep the listing fresh, which conveniently is the subject the next article in this series handles.

The scale is the argument for caring. The App Store averaged roughly 893 million weekly visitors in 2025 with about 464 million weekly search accounts, across more than 2.17 million apps at year end. Google Play held roughly 2.01 million apps as of October 2026, with over 100 billion downloads and an estimated $49.2 billion in consumer spend during 2025, the spend figure being a third-party estimate since Alphabet does not break Play out separately.

What conversion rate should a store listing hit?

Benchmarks keep expectations honest. 2025 US-only data puts average listing conversion at 8.56% on the App Store and 16.15% on Google Play, with category swings from 52.8% for food and drink apps down to 5.2% for trivia games. If your Play listing converts under 10% in a mainstream category, the copy and screenshots are usually the first suspects, before you blame the traffic.

Set your app store description optimization target against your category, rewrite against the structure in this guide, and re-check after each release. When the installs arrive, the funnel keeps going: our push notification copy guide covers the first re-engagement message, and our paywall copy guide covers the screen where readers become subscribers. Teams that systematize this tend to fold listing copy into the same sprint as the update text, which is exactly the pairing the update-notes article walks through.

Frequently asked questions

How long should an app store description be in 2026?

Use the space you earn, not the space you have. Play rewards depth because the field is indexed, so 1,500-3,000 well-structured characters often outperform thin copy. Apple caps the field at 4,000 characters but does not index it, so length should follow persuasion: front-load benefits and stop when the copy stops converting.

Does the App Store description affect keyword ranking?

No. Apple indexes your app name, subtitle, keyword field, and primary category, and combines text relevance with user behavior like downloads and ratings. The description and promotional text affect conversion only, which indirectly helps ranking over time through engagement signals.

What is the character limit for a Google Play full description?

Google Play allows up to 4,000 characters for the full description. The title is capped at 30 characters and the short description at 80. The full description is indexed, so keyword placement and density, roughly 2.5-3% for a primary term, matter far more on Play than on Apple.

Where should keywords go on the App Store?

In the 100-character comma-separated keyword field, plus your name, subtitle, and category. Skip spaces after commas, avoid repeating words already in your name and subtitle, and use singular forms. The description should not be stuffed; Apple treats unrelated keywords there as a quality and compliance risk.

Can ChatGPT write my app store description without triggering keyword-stuffing rejection?

Yes, if you constrain it. Give the model an explicit density ceiling, the list of banned constructs such as ranking claims and emoji, and the store policies to check against, then run a second audit pass. Unconstrained output tends toward repetition, which is precisely what both stores flag as keyword spam.

What makes a good first sentence of an app description?

A concrete benefit in plain language, sized to survive truncation. Before the more tap on Apple and above the fold on Play, readers see only the opening lines, so name the outcome the user gets, not the category you belong to. Compare "Bill every hour your team actually works" with "A project management tool for teams."

How often can I change my app description?

On the App Store, description changes require submitting a new version, but promotional text, up to 170 characters, can be updated anytime without a release. On Google Play, you can edit store listing text without shipping a binary, though recent changes text ships with the release itself.

Do app descriptions matter for AI search engines recommending apps?

Increasingly, yes. Apple generates app tags from your metadata using large language models, and Google Play's Ask Play recommends apps from listing content. Clear, complete, benefit-led sentences give those systems accurate material, while fragmented keyword strings give them nothing reliable to recommend.

Final checklist before you submit

• Both descriptions written separately: search copy for Play, conversion copy for Apple.
• Primary keyword present in title or subtitle, short description, and first Play paragraph, inside 2.5-3% density.
• First sentence states one concrete benefit and survives truncation.
• Every short-field candidate checked against the banned-words list: no best, number 1, prices, deals, calls to action, or emoji.
• Compliance audit run on the final text, including every translated listing.
• Promotional text drafted as your anytime-update channel.
• Custom store listings planned for your top three markets or segments.
• Listing conversion rate recorded now, so the next version proves the rewrite worked.

Run this whole app store description optimization workflow, from feature list to audited bilingual listing, in one place. ArWriter gives you the prompt chain above as ready-made tools with bilingual output, starting at $4.99 per month: https://app.arwriterai.com/

Sources

• Google Play metadata policy for listings — https://support.google.com/googleplay/android-developer/answer/9867158
• App Store product page fields (name, subtitle, promotional text) — https://developer.apple.com/app-store/product-page/
• Custom store listings and AI-generated descriptions on Google Play — https://support.google.com/googleplay/android-developer/answer/13393723
• App Store search documentation and generated app tags — https://developer.apple.com/app-store/search/