Vibe coding mistakes to avoid, and the fix for each

Why do vibe coding mistakes cost more than ordinary coding mistakes?

Because the tool produces plausible output faster than you can check it. In the Stack Overflow Developer Survey 2025, the single biggest frustration with AI tools, cited by 66 percent of developers, was "AI solutions that are almost right, but not quite," and 45 percent said debugging AI-generated code is more time-consuming. Those respondents are professional developers who can read the code. If you cannot, "almost right" is harder to spot and more expensive to fix.

Simon Willison, writing on his blog about what the term should mean, draws the line clearly: "When I talk about vibe coding I mean building software with an LLM without reviewing the code it writes." That definition is useful because it names the risk. The mistakes below are mostly about compensating for the review you are not doing, with product discipline you already have.

Which mistakes happen before the first prompt?

1. Building before you understand the problem

The first prompt is so cheap that it replaces the thinking that should precede it. You describe a solution, the tool builds it, and three hours later you discover the users you had in mind already solve the problem with a spreadsheet they like.

Fix: spend 30 minutes on research first. Ask an AI research tool who has this problem, what they use today, and what the smallest version worth building would be, then turn the answer into a short spec. How to write a PRD for vibe coding covers the research step and a one-screen template.

Stop line: do not open the building tool until you can name the user, the one action they take, and what they use instead today.

2. Letting the scope be "an app that does X, Y and Z"

Every extra feature in the first version multiplies what the tool has to keep consistent. Logins, payments, roles and notifications are the usual culprits, and none of them is needed to learn whether the core idea works.

Fix: one user, one action, one screen where the action happens, one place the result is stored. Write the deferred list down.

Stop line: if the spec mentions a second type of user before the first can complete the core action, cut it.

Which mistakes happen while you build?

3. Vague prompts

"Make a dashboard for tracking habits" leaves every decision to the model, and you find out what it chose by looking at a screen. Lovable's own prompting guide makes the point about content: "Placeholder content like “lorem ipsum” or “feature 1 / feature 2” gives Lovable nothing to respond to."

Fix: name the fields, the empty state, the error state, and the actual words on the main button. Describe interface pieces in specific terms: a card, a modal, a toast after submit.

Stop line: if you could not sketch what your prompt describes on paper, it is too vague to send.

4. Changing several things in one prompt

A prompt that says "add a settings page, fix the date bug and make it look more modern" produces three changes you cannot separate. When something breaks, you do not know which change did it. Built this way, apps become Frankenstein apps: parts bolted on in the order they occurred to you, so fixing one breaks another.

Fix: use big, detailed prompts only at the start, then one bug or one well-defined feature per prompt. Lovable's guide recommends the same habit: "Make one meaningful change at a time."

Stop line: if a prompt contains the word "and" joining two unrelated changes, split it.

5. No checkpoints

Without a saved working version, every failed fix makes the app worse and there is no clean way back. Replit's documentation describes its automatic checkpoints in plain terms: "Think of checkpoints as save points in a video game - you can always go back to a working version of your app." Lovable's guide says to bookmark a version "after each working state" so you have "a known-good state to return to."

Fix: after each feature works, save a checkpoint, bookmark the version or commit to Git. Name it after what works.

Stop line: never start a risky change (a redesign, a new integration, a database change) without a named checkpoint from the last working state.

Which mistakes happen when you think you are done?

6. Testing only the happy path

You try the main flow with sensible inputs, it works, and you share it. Real users type nothing, type too much, delete things in the middle of a list and refresh halfway through a task.

Fix: break it on purpose before anyone else does. Submit empty fields, paste a 5,000 character name, delete the second of three items, refresh mid-flow, open it on a phone. Write down what broke and fix one at a time.

Stop line: do not share a link until the main flow survives five deliberately bad inputs.

7. Shipping output you cannot explain

The code works in your test, so you assume it does what you asked. Often it does something close. Willison states his own rule for production code this way: "I won’t commit any code to my repository if I couldn’t explain exactly what it does to somebody else."

Fix: you may not be able to read the code, but you can ask the AI to explain each part in plain language, then check whether the explanation matches your spec. Where they differ, the explanation usually reveals a shortcut, such as a hardcoded value where you asked for an input.

Stop line: if you cannot describe in plain words how the app calculates its main output, it is not ready.

8. Forgetting secrets, data and money

API keys pasted into the front end, personal data stored without thought, and paid APIs called in a loop are the mistakes with real consequences. Willison warns: "I’ve seen horror stories about people who vibe coded a feature against some API without a billing limit and racked up thousands of dollars in charges."

Fix: set billing limits on every paid API before connecting it, keep keys in the tool's secrets settings rather than in prompts or code, and keep real personal data out of prototypes.

Stop line: anything that handles payments, logins or personal data gets reviewed by someone who can read code before real users see it. When not to use an AI coding agent covers where that line sits.

What do the fixes look like side by side?

Mistake	Symptom you will notice	Fix	Stop line
No research	You are building something nobody asked for	Research, then a one-screen spec	Name the user, action and current alternative first
Bloated scope	The build is nearly working in four directions	One user, one action, one screen	Cut any second user type
Vague prompts	The tool keeps guessing wrong	Name fields, states and copy	Could you sketch it on paper?
Several changes per prompt	A fix breaks something else	One change per prompt	Split any prompt with two unrelated asks
No checkpoints	You cannot get back to what worked	Save after every working feature	No risky change without a named checkpoint
Happy path only	Users find bugs in minutes	Break it on purpose	Survive five bad inputs before sharing
Unexplained output	It works but not quite as asked	Ask for plain-language explanations	Describe the main calculation yourself
Secrets and money	Leaked keys, surprise bills	Billing limits, secrets settings, no real data	Code review before payments, logins or personal data

Is vibe coding itself the mistake?

The strongest objection is that the whole practice is the error: if 66 percent of professional developers find AI output "almost right, but not quite," a non-engineer building without review is asking for trouble. The same Stack Overflow survey found that 72 percent of respondents are not vibe coding in their professional development work.

The objection is right about production software and wrong about everything before it. Willison, who is careful about AI code in production, is also direct about where vibe coding fits: "For low stakes projects and prototypes why not just let it rip?" A prototype that tests whether users want something, an internal tool for five colleagues, or a demo for a leadership meeting are exactly those projects. The mistakes above are what turn a low-stakes project into a high-stakes one, usually without anyone deciding to.

For what the failure looks like in practice, see vibe coding for product managers. When something is already broken, how to debug AI generated code gives a step-by-step loop.

Where can you practise avoiding these mistakes with a real build?

The vibe coding practice exercise is a good test of several of these mistakes at once. It comes from Builders Camp's Zero to Shipped with Vibe Coding bootcamp, where a product manager built a meeting cost calculator in 45 minutes, left it half-working and broken in four ways, and you have 60 minutes to get it ready for a leadership presentation.

The bootcamp itself runs 1 week with 2 live sessions and 11 self-paced microlessons, directed by Andre Albuquerque, and sits in the Vibe Coding Expert Track. Its published syllabus covers scoping the smallest shippable MVP, prompting for building, backend logic and integrations, shipping and deployment basics, and the post-ship iteration loop. The tools it names include Replit, Cursor, Supabase and Stripe.

Before your next build, write the stop line for mistake 6 on a sticky note. It is the one people skip when a demo is an hour away.