AI breaks my application with every change: why a method beats a better model

You request one fix and get three failures. The reflex is to switch tools, then models, then tools again. Those who escaped the loop describe a writing habit costing one line per request.

A user writes on Reddit (r/lovable, June 2026) that credit consumption fell by roughly half when they added ‘do not touch anything else’ to every request. When AI breaks your application with every change, the request's scope is the first place to look, before model choice.

The loop everyone describes

The typical story circulates in an account repeated verbatim across several threads on the same day (August 2026): ask to repair login, login is repaired and data has disappeared; ask to repair the data, three files appear with a dependency nobody has heard of, and nothing works anymore. Its author says they went through eight tools before stopping. A French developer describes the same mechanics from the other side (r/developpeurs, May 2026): their manager intervenes with vibe coding in projects they maintain, the projects lose structure and maintenance becomes hell. Both describe a product whose scope was never written anywhere.

What AI does when nobody bounds the request

The author of the first credit account states it precisely: AI likes being helpful, so you request one change and it improves four other things you never mentioned, half of which break, after which you spend credits undoing its generosity. Give AI a sentence and an entire project, and it treats the whole project as its playground. It has no written boundary: nothing says the login screen, customer table and discount calculation must remain untouched. Every loop expands the area it believes it should improve, and you discover breakage days later on a screen not reopened since the change. Finding which request caused which failure then costs more than the failure itself.

Scope: the cheapest safeguard

Bounding a request costs one line and works in any tool, including those we recommend for quick mockups. Name the file or screen, state what must remain identical and request the list of changes before accepting. For a three-screen application, this discipline often suffices, and chat remains the fastest route. It reaches its limit when your product has fifteen screens, different account permissions and calculation rules known only to you: you can no longer keep the unchanged-items list in your head.

What a test written before code locks down

A test written before construction is a verifiable sentence: ‘a deleted customer disappears from the list but their invoices remain viewable’. Once established, it applies to every subsequent change, including those requested six months later. At Maestro, Félix writes a stage's tests before Amélie writes a single line, and a change breaking an established sentence stops there, naming what it broke. The memory of what the product must do lives in requirements and tests, not your recollection of the conversation two days ago.

The safety net below: return to the previous version

Some things will still slip through. The second safeguard is being able to go back without negotiating with the tool: Maestro keeps a product version at every milestone, and the timeline restores yesterday evening's state, documents included, in one click. A change then becomes a bet whose maximum cost you know. Several prompt generators lack this ability, with rollback dependent on what the platform chose to retain, and it is one of the five documented traps of AI construction.

In practice

Tonight, take the last change that broke something and write one sentence describing what should have stayed intact. You have written your first test. Do the same for the three behaviours your business depends on, signup, payment and data export, and keep those sentences in a document you reread before every request. A better model will shorten the loop; it will not open it for you.