App Store and Google Play ranking algorithms have quietly become AI-driven. Apple's Search Ads and Google's Play ranking use semantic search, behavioral signals, and machine-learned relevance models that no longer reward keyword stuffing the way they did in 2020. According to Apple's official App Store Search Ads documentation (developer.apple.com/app-store/search-ads), relevance is now matched across title, subtitle, keyword field, and in-app purchase names. Google Play's official guidance (support.google.com/googleplay/android-developer) emphasizes app quality signals, retention, and store listing experiments. In practice, that means ASO in 2026 is equal parts metadata craft, review management, and funnel analytics — and LLMs are now a genuine productivity multiplier for all three.
Below is a battle-tested collection of prompts for ASO specialists and mobile marketers. Each prompt is copy-paste ready, includes the task it solves, and a usage example grounded in real App Store Connect and Google Play Console data.
1. Competitor Metadata Teardown
Task: Extract and compare the metadata of top-10 competitors in your category.
Prompt:
You are a senior ASO analyst. I will paste the store listings of 10 competitors in the [category] niche. For each one, extract: title, subtitle (iOS), short description (Android), keyword field (if visible), number of ratings, average rating, and last update date. Then produce a comparison table and identify: (1) keywords that appear in 5+ titles, (2) keywords no competitor uses, (3) title length patterns (character counts). Return the table first, then the gaps.
Example: For a habit-tracking app, this prompt surfaced that 7 of 10 competitors used the word "routine" but none used "streak recovery" — a niche with high search intent and low competition.
2. Title + Subtitle Generator (iOS)
Task: Generate 10 title/subtitle pairs within Apple's character limits (30/30).
Prompt:
Generate 10 title and subtitle pairs for [app name], a [category] app that helps [target user] achieve [outcome]. Constraints: title ≤ 30 chars, subtitle ≤ 30 chars, no repeated words across the pair, include the primary keyword [keyword] in at least 4 pairs. For each pair, explain in one sentence why it should win the tap.
3. Google Play Short Description Optimizer
Task: Write a 80-character short description that reads naturally and ranks.
Prompt:
Write 5 variants of an 80-character Google Play short description for [app]. Primary keyword: [keyword]. Secondary keywords: [list]. Each variant must be a complete, grammatical sentence (no keyword lists). Rank them by likely CTR and explain the ranking.
4. Long Description with Keyword Density Map
Task: Build a 4000-character description with a deliberate keyword map.
Prompt:
Draft a Google Play long description (up to 4000 chars) for [app]. Structure: hook paragraph, 3 feature blocks, social proof block, CTA. Primary keyword [X] must appear 4-6 times, secondary [Y] and [Z] 2-3 times each. After the draft, output a keyword density table with counts and percentages. Flag any block that reads unnaturally.
5. Localization for Tier-1 Markets
Task: Adapt metadata for DE, FR, JP, and BR markets.
Prompt:
Localize the following App Store metadata for the German, French, Japanese, and Brazilian Portuguese markets. Do not translate literally — adapt idioms, cultural references, and value propositions. Keep title ≤ 30 chars and subtitle ≤ 30 chars in each language. Return a table: market
| title | subtitle | keyword field | notes on cultural adaptation.
Example: A meditation app's literal translation of "mindfulness" into Japanese felt clinical; the adapted version used the term マインドフルネス alongside 瞑想 (meditation), which matched actual search volume in Japan.
6. Review Response at Scale
Task: Draft on-brand responses to 1-3 star reviews that also improve ranking signals.
Prompt:
You are a customer success lead for [app]. Here are 15 recent 1-3 star reviews. Group them into themes (bug, missing feature, pricing, UX confusion). For each theme, write one response template (max 400 chars) that: acknowledges the issue, states a concrete next step, and invites the user to contact support. Tone: warm, specific, no corporate fluff. Then list which themes should also become release notes.
7. Rating Recovery Campaign Prompt
Task: Build an in-app prompt strategy that complies with Apple's SKStoreReviewController guidelines.
Prompt:
Design a rating-prompt strategy for [app] using Apple's SKStoreReviewController and Google Play's In-App Review API. Specify: trigger events (list 3), cooldown rules, and copy for the pre-prompt screen. Remind me of Apple's rule: max 3 prompts per 365 days per user. Output as a table: trigger
| timing | copy | platform.
8. Icon and Screenshot A/B Test Hypotheses
Task: Generate testable hypotheses for Google Play Store Listing Experiments.
Prompt:
I run a [category] app with a 4.3 rating and 12% install-to-registration rate. Generate 6 A/B test hypotheses for icon and screenshot variants. For each: the variable changed, the hypothesis, the expected metric movement, and the minimum sample size to reach significance at 95% confidence. Do not invent benchmark numbers — give ranges and cite the method (e.g., two-proportion z-test).
9. Install Funnel Diagnostic
Task: Turn App Store Connect / Google Play Console funnel data into a diagnosis.
Prompt:
Here is my 30-day funnel: impressions [X], product page views [Y], installs [Z], first session [A], day-1 retention [B], day-7 retention [C]. Benchmark against typical [category] ranges (state if you are unsure). Identify the single biggest drop-off and propose 3 concrete changes to fix it. Format as: drop-off stage
| likely cause | fix | metric to watch.
10. Keyword Field Optimizer (iOS 100-char limit)
Task: Fill the 100-character keyword field without wasted characters.
Prompt:
Optimize my iOS keyword field. Current: [paste]. App category: [X]. Target keywords: [list]. Rules: no spaces after commas, no repetition of words already in title/subtitle, singular forms only, no competitor brand names. Return: optimized field, character count, and a list of keywords I dropped and why.
11. Release Notes That Help Ranking
Task: Write "What's New" text that reinforces keywords and reduces churn.
Prompt:
Write release notes for version [X] of [app]. Changes: [list]. Constraints: 500 chars max, mention primary keyword [Y] once naturally, lead with user benefit not engineering detail, end with a light CTA to update. Then write a 170-char version for the App Store "promotional text" field.
12. ASO Audit Checklist Generator
Task: Produce a repeatable monthly audit checklist for your team.
Prompt:
Create a monthly ASO audit checklist for a [category] app on both stores. Include: metadata freshness, keyword ranking movement, review sentiment trend, screenshot/icon test status, crash rate from Google Play Console, and competitor changes. Output as a Markdown table with columns: check
| tool | owner | frequency | red flag threshold.
The thread running through all twelve prompts is simple: LLMs are excellent at pattern-matching across metadata, reviews, and funnel numbers — but they are not a substitute for the data itself. Pull real numbers from App Store Connect, Google Play Console, and Apple Search Ads; feed them to the model; then verify every claim before shipping. That loop — data in, prompts through, human judgment out — is what separates apps that climb the charts from apps that quietly stall at rank 200.
If you want to go deeper into AI-assisted marketing workflows, asibiont.com/blog has hands-on guides on prompt engineering, analytics automation, and AI agents for business. Start with one prompt from this list this week — the competitor teardown is the fastest win — and measure the delta in your keyword rankings after 14 days.
Comments