Vibe-to-Production · Bugs & regressions

Every Fix Breaks Something Else. Here's Why — and the Way Out

Changes used to take minutes. Now you dread them. You fix the thing the customer reported, and two days later a different customer reports something you didn't touch — except apparently you did. Quietly, you've stopped making the product better, because making it different keeps making it worse.

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Bugs & regressionsProblem guide

When every fix to an AI-built app breaks something else, it is because the same logic exists in multiple places and nothing automatically proves the rest of the app still works after a change. You can verify both conditions tonight — count the copies, ask for the test — and the cure is regression tests plus targeted consolidation, not the rebuild you may be dreading.

How to recognise it

The signs show up before the incident does.

None of these proves you have the problem. Each one raises the odds — and every one of them is checkable in the twenty-minute protocol below.

  • Fixes have side effects in strange placesYou change how dates look on one screen and a report two screens away breaks. The distance between cause and effect makes no sense to you — which is the tell that the code's map and your mental map differ.
  • The same bug keeps coming backYou'd swear you fixed this exact thing before — because you did. It was fixed in one copy of the logic, and a later change routed users through another.
  • "Done" now needs a full tour of the appAfter any change, someone has to click through everything to feel safe — and the tour keeps getting longer while coverage keeps shrinking, because humans check what broke last time, not what will break next time.
  • Estimates have stopped meaning anythingTrivial changes take days; scary ones sail through. Effort no longer correlates with apparent size, because the real cost is discovering what each change secretly touches.
  • You've started saying no to your own ideasFeatures get declined not because they're hard, but because touching that part of the app is frightening. The roadmap is being written by fear of the codebase.
Why this happens

One missing layer explains every symptom above.

The same logic lives in many places — and nothing automatically proves any of them still work.

Each generated feature carried its own copy of the logic it needed, so one concept became five near-identical implementations. Every fix must now be correct five times, verified entirely by hand — and each copy someone forgets is the next regression, shipped with full confidence.

This is not a defect of any one tool — it follows from how prompt-built apps come to exist, and the platform guides show how the same gap surfaces on each stack.

What is actually at stake

It isn't a bug count. It's an open door.

This problem's currency is velocity. Every change costs more than the last, so the product slows down at exactly the phase where speed is the whole advantage of building with AI — and competitors on cleaner foundations compound while you tread water. Left alone it ends in the quiet failure mode: a product nobody dares change, maintained but no longer improved, losing to whoever can still ship.

If something has already gone wrong, the first-response guide covers the first hour. If nothing has — that is exactly the moment this is cheap to fix.

Check it yourself

Run the audit yourself — tonight.

No developer, no tooling, no permission needed — this is your own app. Work through the sheet in order; if a check fails, do it once more before believing it. What you find is yours to act on, with us or without us.

Diagnostic protocolAbout 20 minutesTwo browser windowsNo code required

  1. How many places does one concept actually live?

    Run it

    Pick something visible all over your app — how a price is formatted, how a customer's name is displayed. Ask your AI tool, in plain words: "list every file that implements this, and tell me if the implementations differ." No code reading required — just count what comes back.

    What the result means

    One shared implementation is a pass. Four or five copies means every future change to that concept must be made — correctly — four or five times, and the copy someone forgets becomes a bug report. This one question measures the whole problem.

  2. Has any bug come back after being fixed?

    Run it

    List the last handful of bugs you fixed, from memory or your support inbox. For each, ask honestly: is this the first time I've fixed this, or a repeat customer?

    What the result means

    All first-timers is a pass. A single returning bug proves the mechanism directly — it was fixed in one copy of the logic while another kept the old behaviour, or nothing pinned the fix in place. Either way, the fix didn't stick because nothing holds fixes in place.

  3. Does a test exist for the last bug you fixed?

    Run it

    Take your most recent bug fix and ask your AI tool or developer: "show me the automated test that fails if this exact bug ever comes back." You're not evaluating the test — just establishing whether it exists.

    What the result means

    A named, runnable test is a pass. "There isn't one" means every fix you've ever shipped is provisional — nothing stands guard over it, and any future change can silently undo it. This is the root cause, observed directly.

  4. How do you know a change didn't break anything else?

    Run it

    Make a genuinely trivial change — reword a button — then stop and ask: what, concretely, tells us the rest of the app still works? Write the actual answer down.

    What the result means

    "An automated suite runs and passes" is a pass. "We click around" means verification is manual, shrinking, and biased toward last week's breakages — and every release is a bet that this change is the harmless kind. You can't know which kind it is; that's the point.

  5. Could a new person run the project from what's written down?

    Run it

    Open the project's README or setup notes and follow them literally, as a stranger would, noting the first place the instructions stop matching reality.

    What the result means

    Instructions that work is a pass. If setup lives in someone's head, so does everything subtler — which parts are fragile, what must never be touched — and the project can't survive its author's holiday, let alone their departure.

Each of these maps to a scored item in the production-readiness framework. A pass on all of them rules out the loudest failure class — the full audit covers what a browser can't reach, at a fixed price.

How it gets fixed

Sealed from the data layer up.

The order matters more than the effort: enforcement first, credentials second, proof third. Done in the wrong order, the work gets done twice while the exposure stays open. The same discipline runs through everything we deliver.

Get my scorecard
  1. 01

    Map the duplication before touching it

    Engineers chart where logic is repeated, where the copies have drifted apart, and which regions of the code the regressions cluster in — so consolidation follows evidence, not vibes. That map arrives with the audit, in 72 hours.

  2. 02

    Pin down current behaviour with tests — first

    Before any code moves, the critical paths get automated tests asserting what the app does today. This ordering is the whole trick: it makes every later step verifiable instead of hopeful, which is exactly what's been missing.

  3. 03

    Consolidate the copies, worst regions first

    Duplicated logic collapses into single shared implementations, starting where the regression map says changes hurt most. Each merge is proven safe by the tests from the previous step — refactoring with a net.

  4. 04

    Make the tests a gate, not a suggestion

    The suite runs automatically on every change, and a failure blocks the release. From this commit onward, the regressions you've been living with become mechanically impossible to ship silently.

  5. 05

    Institute the one-bug-one-test rule

    Every future bug fix lands with a test that fails if the bug returns. Coverage then grows exactly where reality proves it's needed — and 'fixed' finally means permanently.

Where it sits in the framework

The framework's heaviest weights sit exactly here.

25 / 100

points of the production-readiness score sit in the categories this problem touches.

See every check and weight
0The full 100-point framework100
  • 15 ptsAutomated testingTests are what make the next change safe. Without them, every improvement carries the risk of a regression nobody notices until a customer does.
  • 10 ptsCode quality & maintainabilityThis is the compounding one. It does not break anything today; it decides how expensive every future change is.
Our honest read
If the same bug has come back even once, you don't have a bug problem — you have a duplication-without-tests problem, and the escape is cheaper than the rebuild you're dreading: pin behaviour with tests, then consolidate under their protection.
FAQ

The questions that follow.

QIs it faster to just rebuild the whole thing from scratch?
Usually not, and the intuition that it would be is worth distrusting — a rebuild discards the most valuable thing you own, which is code whose behaviour real users have validated, in exchange for code with brand-new unknown bugs. The test-then-consolidate path keeps the validated behaviour and fixes the structure underneath it. Rebuild becomes the right call only when the foundations genuinely can't carry what's next; the signs it lists are worth reading before deciding, not after.
QWhy does AI-generated code end up duplicated like this?
Because each generation is scoped to the feature you asked for. When you prompted for the invoice screen, the tool generated everything that screen needed — including its own copy of logic that already existed elsewhere, which it had no strong reason to go looking for. Do that across forty features and the same concept exists in many dialects. It isn't carelessness, yours or the tool's; it's what feature-by-feature generation produces unless someone periodically consolidates — the step no prompt ever asks for.
QCan I ask the AI tool to refactor its own code?
Not safely without tests, and understanding why protects you from a very expensive mistake: refactoring means changing structure while preserving behaviour, and without tests nothing defines what the current behaviour is. You'd be asking the tool to preserve something unmeasured — at scale, quickly, with confidence. With a test suite pinning behaviour down first, tool-assisted refactoring becomes genuinely effective, because every proposed change gets checked against the net. Tests first is not a preference; it's the load-bearing order.
QHow much testing is enough? Do we need to cover everything?
No — and chasing total coverage is its own trap, burning weeks on tests of trivial code. The pragmatic bar: the critical path your business depends on (sign-up, the core action, payment if you take money), any logic that exists in one consolidated place precisely because many screens rely on it, and a test for every bug you fix from now on. That last rule quietly does the most work — it grows coverage exactly where reality demonstrates fragility.
QWe keep shipping features. Should we stop until this is fixed?
No — a feature freeze usually costs more than the problem, and it isn't necessary. The fix runs alongside normal work: tests land first on the paths you're actively changing, consolidation proceeds region by region, and each merged copy makes the next feature cheaper. What is worth pausing is any large feature that would build on top of the worst-duplicated regions — extending those before consolidating them is buying more of the problem at full price.
QWhat does this cost, and how long until changes stop being scary?
The audit ($2K – $5K) delivers the duplication map, the regression clusters, and the ranked consolidation plan within 72 hours. The engagement itself typically runs 2–6 weeks depending on codebase size — but the fear curve bends early: the moment the critical path has tests, changes stop being bets, and that lands in the first stretch of the work, not the last.
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