The Vibe Coding Practices That Still Work on Day 30

Almost every list of vibe coding tips is really a list of prompting tips. Be specific. Give examples. Ask it to think step by step.

That advice isn't wrong, it's just aimed at the wrong problem. Prompting quality determines how good your first afternoon goes. It has very little to do with whether you still have a working app in a month.

The failures that kill projects are structural. You lose track of how the pieces fit, the AI loses track at the same time, and neither of you notices until something breaks that nobody can explain. These seven practices are the ones I'd keep if I had to throw the rest away.

1. Decide what you're building before you ask for any of it

The strongest correlation I've seen with whether a project survives isn't tool choice or prompting skill. It's whether there was a plan before the first prompt.

Not a document. A plan, meaning you can answer four questions:

What screens exist? What data does the app store? What can a user do? Who's allowed to do what?

Ten minutes of thinking gets you rough answers to all four. The value isn't the answers, it's that you now have somewhere to put things. When you ask for a feature you know which screen it belongs on and which table it touches. Without that, every feature request is a fresh negotiation about what the app even is, and the AI will happily invent a different answer each time.

This is why the planning module in our course runs eleven lessons before anyone writes a line of code. It looks like a detour. It's the thing that decides whether week three is productive or spent untangling.

2. Get the data model roughly right early

Everything else can be changed cheaply. Your data model can't.

Change a button colour, that's one file. Change a page layout, maybe three. Realise in week three that a project should belong to a team rather than a user, and you're touching your schema, your policies, every query, and most of your screens.

You don't need it perfect. You need it approximately right on the things that are expensive to reverse, which is mostly ownership: what belongs to what. Ask yourself whether anything will ever be shared, whether anything needs history, and whether one user might have several of a thing you're currently assuming they have one of.

Sketch the tables and their relationships before you build the screens that depend on them. Half an hour here routinely saves a rewrite.

3. Change one thing, then click through everything

The classic failure mode is a long session with a dozen changes, at the end of which three things are broken and you can't work out which change did what.

Small changes, tested immediately, mean you always have exactly one suspect.

Testing means using the app, not looking at it. Click the buttons. Submit the forms with bad input. Log in as a second user. The preview rendering without errors tells you the code compiled, which is a much weaker claim than it feels like.

I'd rather someone made six changes and clicked through six times than made twenty and clicked once. The second person moved faster for about an hour.

4. Commit constantly, and write real messages

Version control is the difference between "that broke, let me undo it" and "that broke, let me try to remember what it looked like before."

Commit every time something works. Not at the end of a session, not when a feature is done. Every time the app is in a state you'd be sad to lose.

Write messages that describe what changed, because in three weeks you'll be scanning the list looking for the commit just before things went wrong. "Fix" tells you nothing. "Add role check to project delete endpoint" tells you exactly whether it's the one you want.

If your builder pushes to GitHub, connect it on day one. The value is entirely in the history existing before you need it.

5. Start a new chat when the model starts contradicting itself

Long conversations degrade. The model starts referencing files that no longer exist, reintroduces code you removed an hour ago, or forgets a decision you made at the top.

That's the signal. Not a fixed message count, just the moment the answers stop being consistent with your project.

Start a fresh conversation and give it a short orientation: what the app is, which files matter for this task, what you're trying to do. Ninety seconds of context beats an hour of arguing with a confused thread.

Some tools let you keep a persistent project instructions file. Use it. Put your stack, your conventions, and your data model in there so every new conversation starts from the same footing.

6. Assume it will not mention security

Your feedback loop has one signal: does the app do the thing. The model optimises for that, because that's what you're rewarding.

A secure app and a wide-open app both satisfy that signal identically. They look the same in the preview, they behave the same when you test them, and nothing turns red.

So the security check has to be a separate deliberate pass. At minimum, before anything goes live: confirm Row Level Security is enabled on every table, read your policies for using (true), and grep your build output for API keys.

That takes fifteen minutes and it's the difference between an app and a data breach. I've written the full pre-launch checklist and a deeper piece on RLS, because this is where AI-built apps fail most often and most expensively.

7. Read the code, even when you can't write it

You don't have to be able to produce the code from scratch. You do have to be able to look at what appeared and form an opinion.

Reading is a much lower bar than writing, and it develops fast. After a few weeks of skimming diffs you'll notice things without trying: a query with no user filter, a check running in the browser that should be on the server, a second function doing what an existing one already does.

The alternative is accepting changes you haven't examined, which works until it doesn't, and by then you have a codebase where you can't distinguish the parts you understand from the parts you're hoping about.

Skim every diff. Ask what a line does when you don't recognise it. That's it. That habit alone is most of the gap between people who ship and people who stall.

What ties these together

Six of these seven are about one thing: keeping a model of your own project in your head.

The plan gives you the shape. The data model gives you the structure. Small changes keep you oriented. Commits give you a way back. Fresh chats stop the AI from drifting away from your understanding. Reading the code keeps your understanding current.

The AI is very good at writing code and completely indifferent to whether you understand it. Nothing in the tool will push you toward comprehension, and quite a lot will let you skip it. Skipping is fine on day one. It's the entire problem by day thirty.

Speed on the first afternoon is not the constraint. Nobody fails because the AI typed too slowly.

Frequently asked questions

What are the most important vibe coding best practices? Plan the screens and data model before prompting, make small changes and test after each one, commit whenever something works, and run a deliberate security pass before launch. Prompting technique matters far less than any of these.

How do I stop the AI from breaking things it already built? Smaller changes with a click-through after each. Start a fresh conversation when the model starts contradicting itself. Keep a project instructions file so context survives between chats. Most "it broke my working code" moments trace back to a long conversation where the model lost the thread.

Should I use an app builder or an AI code editor? Builders like Lovable get you moving fastest and are the right starting point. Editors like Cursor give you more control and are worth moving to when the builder stops you doing something you need. Plenty of people start in a builder, export to GitHub, and continue in an editor.

How much code do I actually need to understand? Enough to read a change and judge whether it's reasonable. Where data lives, what runs on the server versus in the browser, which parts a user can tamper with. Much less than a CS degree, much more than nothing.

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These practices are the spine of VibeMastery, which runs from the request-response cycle through planning, auth, payments, and deployment. It's built for people who can already get an AI to write code and want to stop being surprised by what it wrote.