5 Major Pitfalls of AI Vibe Coding for Mobile Apps: A Guide from Tech Stack Selection to App Store Submission

More and more people are building apps with AI, but what really trips them up often isn't the final submission step—it's the hidden risks planted from the moment they write the first prompt. A practitioner who regularly helps developers without IT backgrounds handle app submissions and rejection appeals (the YouTube channel "Đức Dòng") summarized the five most common mistakes and provided a prompt checklist you can paste directly at the start of your project.

Pitfall 1: Choosing the Wrong Tech Stack—You End Up Building a Web Page, Not an App

AI is very good at generating interfaces. The moment you write "help me build a booking app" in a prompt, it quickly produces something that looks polished, has clickable features, and is even displayed inside a phone frame in the preview. The problem is that this is very likely a React web page, not a real mobile application.

According to the channel's observations, many developers keep investing one or two more months into this "fake app" until they're ready to publish and are told: web projects usually cannot be submitted directly to the App Store or Google Play. At that point there are only three paths—rebuild with mobile technology (paying the time and cost all over again), wrap the web page into an app (faster, but a pure wrapper is easily rejected by Apple for "lacking native value"), or simply give up on publishing and stick with the web version.

The right approach is to explicitly require a mobile tech stack in the very first prompt, rather than circling back after the interface is done.

React is a JavaScript front-end framework open-sourced by Facebook, mainly used for building web user interfaces. Its component-based design and rich ecosystem let AI rapidly generate interactive interface prototypes—which is why AI defaults to using it to "build apps." However, React itself runs in a browser environment and cannot be packaged directly into a native iOS or Android app. To reuse web code on mobile, you typically need bridging solutions like React Native or Capacitor, but these still differ in performance and native capability access, and they don't always pass store review in every scenario. Truly pure mobile development generally uses Swift/SwiftUI (iOS), Kotlin/Jetpack Compose (Android), or cross-platform frameworks like Flutter and React Native. Therefore, explicitly stating the target platform and framework in your prompt is the key prerequisite for avoiding a "web-version fake app."

Pitfall 2: Hardcoding API Keys into the Client—Handing Over Your Wallet

This issue won't cause a rejection—the app can still be published—but it can cost developers a lot of money. When an app needs to call AI capabilities or read/write a database, it usually has to carry an API Key; to make the code "just run," the easiest thing for AI is to hardcode it into the client.

Everything works fine in local testing, but once published, anyone who downloads the app can decompile it and inspect its internals. The channel mentions that there are even people who specialize in bulk-scanning for such leaked keys. The principle is simple: everything in the client should be treated as public information; sensitive keys must live on the server, with the app requesting the server and the server calling third-party services.

If the app is already published and you suspect a key leak, the first step is to revoke the old key in the provider's dashboard and generate a new one—changing the code at this point is useless, because someone may have already taken the key.

An API Key is a credential string used to identify the caller, and providers rely on it for billing, rate limiting, and authentication. Putting it in the client (i.e., the app installation package on the user's device) is dangerous because mobile app code and resources can be decompiled or unpacked, and string constants often remain as-is in the binary. Attackers commonly use automated tools to bulk-scan publicly available installation packages, extract the keys, and call paid APIs directly—with all charges billed to the original developer. The correct architecture is to introduce your own server as a "proxy": the client communicates only with your server, which holds the key and forwards requests, and can also add user authentication, call rate limits, and usage monitoring. Even if the client must hold certain identifiers, use restricted-permission public keys that can be revoked at any time, not master keys with full account privileges.

Pitfall 3: Payment Methods That Violate Store Rules—The Entire Flow Has to Be Rebuilt

A typical scenario: a developer builds an English-learning app and sets up a membership package that unlocks all courses for a fee. Thinking Vietnamese users are used to bank transfers, they have AI paste a bank payment QR code directly into the app. The result: rejection by Apple.

Both Apple and Google have clear rules: any digital content and services consumed instantly within the app—memberships, subscriptions, unlocked content, unlocked features—must by default go through the store's in-app purchase system (In-App Purchase), from which the store takes a commission that varies by country and region. The original source mentions this commission range is roughly 15% to 30%, but the store's current policy prevails.

The exception is selling real-world goods or services, such as haircut appointments or food delivery orders—these can still use ordinary payment channels. So before building the payment page, ask AI: given this business model, what payment method do the App Store and Google Play each require? Otherwise, once rejected, what needs changing isn't just a button but the entire verification logic from the purchase page to "who has paid," plus recalculating the price.

In-App Purchase (IAP) is the in-app purchasing system provided by Apple and Google respectively. Its core mechanism: transactions are handled by the store, the user's payment method linked to their store account is charged, and the store deducts its commission before settling the remainder to the developer. For developers, IAP saves the work of handling bank cards, reconciliation, and refunds yourself—but it also means you can't bypass the commission. When using IAP, the purchase receipt must be verified by your server against the store's verification endpoint to confirm the transaction is genuine before granting the user their entitlement; if you let users through based solely on a "purchase successful" response from the client, it's easily bypassed with forged requests. This is why the source emphasizes that the "who has paid" verification logic must be rebuilt as well—changing the payment method isn't just swapping a button; it affects the entire chain of order verification, entitlement delivery, and refund handling.

Pitfall 4: Missing Store-Mandated "Non-Core Features"

These things aren't the app's main selling points—developers don't think of them, and AI won't add them proactively. Adding them after the app is done often has far-reaching consequences. The channel names the four most commonly omitted mandatory requirements:

• Account deletion: As long as you allow users to register, you must allow them to delete their accounts within the app. You can implement a soft delete that prompts the user on next login that the account will be purged after a certain number of days—physical deletion doesn't have to be immediate.
• Sign in with Apple: On iOS, as long as you integrate third-party logins like Google or Facebook, Apple requires you to also offer a privacy-focused login method—the easiest being a "Sign in with Apple" button.
• Privacy policy and permission declarations: You must explain what information the app collects and for what purpose. Third-party libraries introduced by AI may incidentally trigger camera, location, and other permissions that the app doesn't actually need; before publishing, verify each permission actually in use.
• User-generated content governance: Apps with UGC such as posts, comments, chats, and images must provide features to report content and block other users.

If these four are written into the prompt at the start of the project, AI will account for them when designing the database and pages; if added after the app is complete—especially account deletion—they touch a large amount of already-tested code and easily introduce new bugs.

Pitfall 5: Underestimating the Submission Timeline—Breaking Promises to Clients and Release Plans

This one doesn't involve code, yet it's the most likely to cause you to miss commitments to clients. Many developers assume that once the app is done, they can just hit submit and go live, so they set tight release dates. The reality:

• Google Play: Newly registered individual developer accounts must go through a closed testing phase before official release—at least 12 testers participating continuously for 14 days. Even if the app is flawless, that means a minimum of two weeks, and only if you gather 12 people on day one. The developer account itself requires a one-time fee.
• Apple: There's no similar testing threshold, but the developer account is charged annually. To publish under a company name rather than an individual, you also need to apply for a D-U-N-S number, which can take anywhere from several days to several weeks.

Add identity verification, store review queues, and the time to resubmit after rejection, and promised dates easily slip. It's advisable to register developer accounts on day one of the project so these procedures proceed in parallel with development, and to build at least about a month of buffer into delivery expectations for clients.

A Checklist to Put at the Start of Your Project

To distill the five points into one sentence: mobile tech stack, keys on the server, payments per store rules, mandatory features included, and buffer time for submission. The channel also recommends explaining your business model to AI in the first prompt and proactively asking about store payment restrictions, rather than reworking after the fact. For developers without IT backgrounds building apps from scratch with AI, the value of these five upfront decisions far exceeds any later optimization.

Note: This article is based on content from the YouTube channel "Đức Dòng." The commission percentages and account fee ranges cited are as relayed by the original author; Apple's and Google's current policies prevail.

Writing upfront requirements into the first prompt works because large language models design data structures and page structures based on the constraints given in the conversation when generating code. If you state at the start of the project "built with Flutter, keys proxied through the server, account deletion and report/block features required," the model will often reserve corresponding fields and entry points when creating tables, endpoints, and pages; conversely, adding requirements later can only be done through local modifications, which easily conflict with existing code. In practice, you can compile these requirements into a fixed project brief and attach it every time you start a new conversation or ask AI to modify code, and require AI to restate its understanding of the tech stack, payment method, and mandatory features before generating code—confirming correctness first to reduce rework.