Common AI Coding Bugs to Look for Before Launching Your MVP

Every founder launching an AI-built MVP eventually asks some version of “what should I actually be checking before this goes live?” This is a direct answer: the specific, recurring bugs that show up most often in AI-generated MVPs, in the order worth checking them, so a pre-launch review has a concrete list to work through instead of a vague sense of “test everything.”

1. Broken or Missing Form Validation

The single most common AI coding bug: a form that accepts an empty required field, a malformed email, or a value outside a sensible range, because the validation rule was never explicitly requested. Check every form in the product — signup, checkout, settings, any user-editable field — by deliberately submitting bad input and confirming it’s rejected clearly, not silently accepted or silently rejected with no explanation.

2. Missing Server-Side Authentication Checks

A dangerously common pattern: a sensitive action (deleting a record, viewing another user’s data, changing a setting) is correctly hidden in the interface for users who shouldn’t see it, but the underlying request isn’t actually blocked if someone calls it directly. Check this by testing whether a restricted action can be triggered by a user who shouldn’t have access — not just whether the button is visible to them.

3. Duplicate Submission Handling

Clicking “submit” twice quickly, or a slow network causing a retry, frequently produces two records instead of one, two charges instead of one, or a confusing error state — because the AI wasn’t asked to guard against a duplicate request specifically. This is easy to test: submit the same action twice in rapid succession and confirm only one result occurs.

4. Silent Failures With No Visible Error

Some AI-generated error handling catches a problem internally and shows nothing to the user — the action simply doesn’t complete, with no explanation. This is worse than an obvious crash because it can go unnoticed for a long time. Check by deliberately triggering failure conditions (disconnect the network mid-action, submit something that should be rejected) and confirming an error is clearly shown, not swallowed.

5. Inconsistent Handling of the Same Data Type

If your MVP collects the same kind of information in more than one place — an email address on signup and again in account settings, for example — check whether both places validate and store it identically. Built through separate prompts, these can easily diverge, and the inconsistency tends to surface as a confusing bug exactly where the two features interact.

6. Unverified or Hallucinated Dependencies

Check the package manifest for anything unfamiliar and confirm it’s a real, maintained package doing what it’s assumed to do. AI coding assistants occasionally reference a library or method that doesn’t exist as described — a fast check against the real package registry rules this out before it becomes a production incident.

7. Performance With Realistic Data Volume

Code that’s fast with a handful of test records can slow down significantly, or behave incorrectly, once real usage arrives. Load the product with data closer to expected real volume and check for anything that gets noticeably slower or starts behaving unexpectedly.

8. Missing Confirmation on Destructive Actions

Deleting an account, removing a record, or canceling a subscription without a confirmation step or an undo window is a common gap — the AI implements the delete action correctly but doesn’t add friction around it unless explicitly asked to. Check every destructive action in the product and confirm there’s a deliberate confirmation step, not a single accidental click away from permanent data loss.

9. Error Messages That Don’t Actually Help

Even when errors are shown rather than swallowed silently, they’re sometimes generic to the point of being useless — “something went wrong” with no indication of what or how to fix it. Check the most common failure points (login, payment, form submission) and confirm the error message actually tells the user what happened and what to do next.

Pre-Launch Bug Checklist

#	Bug to check for	Where to look
1	Broken form validation	Every user-facing form
2	Missing server-side auth checks	Every sensitive action, tested directly not just via the UI
3	Duplicate submission handling	Any action that creates or charges something
4	Silent failures	Error handling and logging across the app
5	Inconsistent data handling	Any data type collected in more than one place
6	Unverified dependencies	Package manifest vs. the real package registry
7	Performance at realistic volume	Core flows under realistic data load
8	Missing confirmation on destructive actions	Delete, cancel, and remove flows
9	Unhelpful error messages	Login, payment, and form submission failures

Turning This Checklist Into a Review

Working through this list yourself catches a meaningful share of what matters, but the highest-value step is having someone who didn’t write the original prompts run through it independently — they won’t share the same assumptions about what “should” already work. Debugging AI-generated code before a production launch walks through the fuller step-by-step process this checklist is drawn from, and AI-generated code problems: what founders need to know before launching covers the broader risk categories — including maintainability and cost — beyond bugs alone.

The Takeaway

These seven bugs account for the large majority of what actually goes wrong in AI-built MVPs right after launch, and every one of them is checkable in advance with a specific, deliberate test rather than a general click-through. Running this list before launch, ideally with an independent reviewer, is a small time investment against a much larger cost if any of these surface with real users already depending on the product.

Frequently Asked Questions

What are the most common AI coding bugs found right before an MVP launch?

Broken form validation, missing authentication on sensitive actions, duplicate-submission handling, silent error failures, and inconsistent data handling across similar features are the ones that turn up most often in a pre-launch review of AI-built MVPs.

How much time should I budget for a pre-launch bug check?

For a typical MVP-sized codebase, a focused review against a defined checklist like this one is usually a matter of days, not weeks — significantly less time than fixing the same issues after real users have already hit them.

Can I find these bugs myself without a developer?

You can catch some of them by deliberately testing edge cases yourself — empty fields, duplicate clicks, unusual input. Security and consistency issues, however, are harder to verify without reading the actual code, which is where a developer or reviewer adds the most value.

Should every one of these bugs block launch?

Not necessarily — but anything touching authentication, payments, or user data should be fixed before launch. Lower-severity issues, like a cosmetic edge case in a rarely used flow, can sometimes be tracked and fixed shortly after launch instead.

Are these bugs specific to a certain AI coding tool?

No — the same categories of bugs show up across chat-based builders, editor-integrated AI assistants, and terminal-based coding agents, because they come from what's left unspecified in a prompt rather than from any one tool's implementation.