Vibe Coding: What It Is, How It Works, and Its Limits

Vibe coding is building software by describing what you want to an AI in everyday language, then running whatever it produces and steering it with follow-up prompts instead of writing or reading the code line by line. The AI researcher Andrej Karpathy coined the term in early 2025, and it caught on almost immediately.

Here’s the short version: vibe coding is fast and genuinely accessible for prototypes, personal tools and simple internal automations. Once software handles real users, real data or a business-critical workflow, it needs review, testing and security work before anyone relies on it. This guide covers how the process works, which tools people use, where projects tend to break, and how to turn a promising prototype into something a team can depend on.

What Vibe Coding Means

Karpathy described vibe coding as giving in to the vibes: you tell the AI what you want, accept what it writes without studying it, paste error messages back when something breaks, and keep going until the thing works. Rather than reading the code, you judge the program by how it behaves.

That last part is the whole idea, and it’s where a lot of articles get fuzzy. Two different practices are being described:

• Vibe coding: you prompt, run, react and repeat. You rarely open the code, and you may not be able to explain how it works. The AI owns the implementation and you own the outcome.
• AI-assisted development: a developer uses AI to write code faster, but reads, understands and takes responsibility for every change before it ships. The AI is a fast typist and a sounding board. The human is still the engineer.

Both use the same tools. What separates them is whether anyone understands the result. A professional developer can vibe code a throwaway script on a Friday afternoon and then switch to careful AI-assisted work on the payment system on Monday. Neither one is wrong, as long as the approach fits the stakes.

Why the Term Spread So Fast

The name stuck because it described something lots of people were already doing and had no word for. AI models had become good enough to produce working apps from a paragraph of text, and browser tools made it possible to see the result without installing anything. Suddenly product managers, marketers, founders and operations staff could build software, and “vibe coding” gave that experience a label.

It’s also loosely defined because it started as a casual remark rather than a methodology. Some people use it strictly, meaning you never look at the code. Others use it for any AI-generated software at all. When you read about vibe coding, check which meaning the writer has in mind, because the risks are very different.

How a Vibe Coding Session Works

Every vibe coding session follows the same basic loop:

1. Describe the goal in plain language: what the software should do, who uses it and what a successful result looks like.
2. The AI generates code, or a full app with screens, a database and logic, depending on the tool.
3. Run or preview it. Click through it the way a real user would.
4. React to what you see: an error message, a button that does nothing, a layout that’s off, a calculation that’s wrong.
5. Re-prompt with specific feedback, and repeat until it works.

Let’s walk through a realistic example from everyday business work: a purchase request form where employees submit requests, and anything over a set amount goes to their manager for approval by email.

A Good First Prompt

Vague prompts get vague apps. Compare “make me a purchase request app” with this:

Build a simple web app for internal purchase requests. Employees fill out a form with: their name, their work email, their manager’s email, item description, vendor, estimated cost in USD, and a business reason. Requests under $500 are auto-approved and the employee gets a confirmation email. Requests of $500 or more send an email to the manager with Approve and Reject links. When the manager clicks one, the request status updates and the employee is notified. Include a page that lists all requests with their status, newest first. Keep the design plain and readable.

(The $500 threshold is just an example. Use whatever your policy says.) This prompt works because it names the fields, the rule, the threshold, what happens on each path and what the user should see. The AI has far less to guess.

A Good Follow-Up Prompt

Say the first version runs, but you notice two problems while testing: you can submit a request with a blank cost, and the list page shows requests oldest first. A good follow-up names exactly what you saw and what you expected:

Two fixes. First, the cost field accepts blank and negative values. Make it required, allow only numbers greater than zero, and show an error message under the field if it’s invalid. Second, the requests list is sorted oldest first. Sort it newest first by submission date. Don’t change anything else.

That “don’t change anything else” line matters more than it looks. AI tools sometimes “improve” parts of the app you didn’t ask about, and that’s how working features quietly break.

When the AI Hits an Error

Eventually you’ll click Approve and get something like this:

TypeError: Cannot read properties of undefined (reading 'email')
    at sendApprovalNotification (approvals.js:42)

In a vibe coding session, you paste the full error back with context about what you were doing:

I submitted a $750 request, the manager got the email, and when I clicked Approve I got this error: [paste error]. The status didn’t change and the employee wasn’t notified. Please fix it and explain in one or two sentences what caused it.

Usually the AI finds the cause (here, probably that the code looks up the employee record but the request doesn’t store a link to it) and patches it. Asking for the explanation is a cheap habit that pays off: it helps you notice when the AI is guessing, and it builds up your own understanding of the app.

Types of Vibe Coding Tools

The tools fall into three broad groups. Which one fits depends less on which is “best” and more on how comfortable you are with files, terminals and hosting.

Browser App Builders

Lovable, Bolt.new, v0 (from Vercel) and Base44 let you describe an app in a chat panel and watch it appear in a live preview next to it. Many handle the pieces beginners get stuck on, such as a database, user logins and publishing the app to a URL. If you’ve never written code, this is the easiest place to start, and it’s the fastest way to get a clickable prototype in front of colleagues.

The trade-off is control. You’re working inside the platform’s choices for frameworks, hosting and data storage. When the app needs a custom integration, a specific security setup or a move to your company’s own infrastructure, that can take real engineering work. Most of these tools let you export or sync the code, which helps, but exported code still has to be understood by whoever maintains it.

AI Code Editors and Coding Agents

Cursor and Windsurf are code editors with AI built in: you can chat with your whole project, ask for multi-file changes and review the diffs. Claude Code, from Anthropic, and Aider, an open-source tool, run in the terminal and act as agents that read your files, make changes and run commands. Aider works closely with Git, committing changes as it goes.

These suit developers and technically confident users who want AI speed on a real codebase that lives in version control. They’re also the tools where the line between vibe coding and AI-assisted development is easiest to cross in either direction. The catch is setup: you need a working development environment, and you need enough skill to tell when the agent has done something unwise.

General Chat Assistants

ChatGPT, Gemini and Google AI Studio are great for writing a Python script to clean a CSV file, explaining an error message or drafting a spreadsheet formula. Google AI Studio also has a build mode aimed at generating small apps from a prompt. For most tasks, though, these assistants hand you code and leave the rest to you: saving files, installing dependencies, running it and hosting it somewhere. That’s fine for scripts you run once, and awkward for anything with users.

What Vibe Coding Is Good For

Vibe coding shines when the cost of being wrong is low. Good fits include:

• Clickable prototypes and MVPs. You can test whether an idea makes sense to users before anyone commits budget to building it properly.
• Personal productivity tools. A custom tracker or calculator only you use can be as quirky as you like.
• One-off data cleanup scripts. Deduplicating a contact list or reformatting dates in an export is a task you run once, check the output of, and throw away.
• Internal dashboards and reports. Read-only views of data you already have carry little risk, especially if they don’t write back to source systems.
• Small automations that remove manual work. Copying data between spreadsheets, generating a recurring report, routing approvals or doing first-pass ticket triage are all well-defined tasks with clear inputs and outputs.

The common thread: low stakes, a small number of users, and something you could easily throw away or rebuild if it went wrong. As soon as a project stops matching that description, it needs a different level of care.

Why Vibe-Coded Projects Fail

When people ask why vibe coding “failed,” they usually mean a specific project that worked in the demo and then fell apart. The causes are predictable.

The Doom Loop

You ask the AI to fix a bug. It fixes it and breaks the login page. You ask it to fix the login page, and the original bug comes back. Twenty prompts later, the app is worse than it was an hour ago. This happens because each fix is made without a full model of the system, and long conversations make the AI lose track of earlier decisions. If you can’t read the code, you can’t spot the pattern, so you keep prompting.

Nobody Understands the Code

Picture the purchase request app six months in. Finance wants a second approval step above a higher threshold. The person who built it has moved teams, nobody has ever read the code, and the AI’s attempt to add the step quietly changes how existing approvals are recorded. Software that no one can explain is software no one can safely change.

Security Gaps

AI-generated code tends to make the happy path work and skip the defenses. Common gaps include:

• API keys or passwords written directly into the code, where anyone who sees the code (or the browser’s developer tools) can find them
• Missing authentication, so anyone with the URL can use the app
• Missing authorization, so any logged-in user can see or approve anyone’s requests
• Unvalidated input that lets bad data, or malicious data, straight into the database
• Database access rules that are far broader than the app needs

The OWASP Foundation publishes free guidance on the most common web application security risks, and it’s a good reference for knowing what to check.

Edge Cases That Only Show Up With Real Data

The demo used tidy test data. Real users submit costs with dollar signs and commas, names with apostrophes, managers who’ve left the company and duplicate requests from double-clicking Submit. Each of these can crash the app or produce wrong results, and none showed up in testing because nobody tried them.

Fragile Dependencies and No Tests

Generated apps often pull in many third-party libraries, sometimes outdated ones. Without automated tests, there’s no quick way to know whether an update, or the next AI-generated change, broke something. You find out when a user does.

Data Privacy Risk

To get a fix, it’s tempting to paste in a real spreadsheet of customer orders or an employee list. Depending on the tool and your account settings, that data may be stored by the AI provider or used in ways your company’s policies don’t allow. Check the provider’s data-use terms, and treat anything containing customer, employee or financial information as off-limits for public tools unless your company has approved it.

Is Vibe Coding Legit, and Can Anyone Do It?

Yes, it’s legit, with a clear boundary. Vibe coding is a real, practical way to build prototypes and simple tools, and people with no programming background are producing working software with it every day. That’s a meaningful change: an operations manager can now test an idea in an afternoon instead of waiting months for a development slot.

What it doesn’t do is replace engineering judgment for production systems. Someone still has to decide how the app should handle failure, who can access what, and whether the code is safe to run against real data.

Can anyone do it? Anyone can start. Doing it well depends on skills that have nothing to do with syntax:

• Describing requirements precisely. If you can write a clear process document, you can write a clear prompt.
• Testing behavior. Try the weird inputs, the wrong user and the double click, not just the happy path.
• Recognizing wrong output. A report that runs without errors can still have the wrong totals. You need to know what right looks like.
• Knowing when to bring in a developer. This is the most underrated skill of all. The moment real users, money or sensitive data enter the picture, get someone who can read the code.

Using Vibe Coding to Automate Business Processes

One of the most useful places for vibe coding is operations: the approvals, data entry, reporting and handoffs that eat hours every week. It works well for quickly testing whether an automation idea holds up. Getting from that test to something a team relies on daily takes a few more steps.

Describe the Process Before You Prompt

The quality of an automation depends on how well you describe the process. JAMD Technologies, which publishes this guide, uses a discovery approach for workflow projects that also works well as a structure for prompts:

• A one-sentence problem statement naming the workflow, the users and the business impact. For example: “Accounts payable clerks rekey invoice data from email PDFs into NetSuite, causing late payments and avoidable errors.”
• The trigger: what starts the work, such as an email, a form submission, an EDI file or a phone call.
• Steps and handoffs: who touches it, in what order, and where it stalls.
• Systems involved: Excel, SharePoint, Salesforce, NetSuite, QuickBooks, ServiceNow, Jira, or a legacy SQL database.
• The top exceptions that create rework: missing fields, duplicates, approvals, returns. Aim for the top five.

Feed this into your prompt and the AI has a real specification instead of a wish. Better still, the exceptions list doubles as your test plan: each one is a scenario you should try before calling the automation done.

Common Integration Patterns, Explained Simply

Most business automations connect systems in one of a few ways. Knowing the names helps you prompt accurately and understand what the AI built.

• APIs: one system asks another for data or tells it to do something, such as creating a record in Salesforce or fetching an invoice from QuickBooks. Most modern business software offers one.
• Webhooks: instead of your automation constantly checking for changes, the other system calls your automation when something happens, like “a new ticket was created.”
• Scheduled jobs: code that runs on a timer, such as every night at 2 a.m. or every Monday morning. Good for recurring reports and batch syncs.
• Data validation: checks that reject or flag bad data before it reaches the next system. This is the pattern vibe-coded automations skip most often, and it’s the one that prevents the most cleanup.

Some teams also use RPA (robotic process automation) tools that click through screens like a person would. These are useful when a legacy system has no API, but they break when the screen layout changes, so prefer an API when one exists.

Quick Automation vs. a Workflow a Team Can Depend On

A script that works when you run it and a workflow your team relies on daily are different things. Here’s what separates them:

Idempotency deserves a word of its own because it causes so many real headaches. Say your automation creates a vendor bill in NetSuite, then fails while sending the confirmation email. If it reruns the whole thing, you now have two bills. A dependable workflow checks whether the bill already exists before creating it.

Measure Whether It Worked

Before you switch anything on, capture a baseline: how long the process takes today, how many items go through it and how often errors or rework happen. Collect the same measures after the automation has run for a while and compare. Without the “before” numbers, you’ll only have a feeling that things improved, and that’s a hard case to make when someone asks whether the automation is worth maintaining.

Taking a Vibe-Coded Prototype to Production

If your prototype is about to get real users or touch real data, work through this checklist first:

1. Get a code review from someone who can read the code. They don’t need to rewrite it, just understand it and flag problems.
2. Remove secrets from the code. Move API keys, passwords and tokens to environment variables or a secrets manager, and rotate any keys that were ever exposed.
3. Add authentication and role-based access so only the right people can sign in, and each person can only see and do what their role allows.
4. Validate every input, on the server as well as in the form.
5. Write tests for the main paths and the known edge cases, including the exceptions from your process description.
6. Add error handling, logging and monitoring so failures are caught, recorded and reported to someone.
7. Put the code in version control (Git, with a host such as GitHub or GitLab) so every change is tracked and reversible.
8. Document what the software does, what systems it touches and who owns it.
9. Review where data flows, including into AI tools. If the app or its development touches sensitive data, decide whether that data should go only to self-hosted or private AI models rather than public tools.
10. Plan for maintenance. Dependencies need updates, connected systems change their APIs, and business rules evolve. Someone has to own that work.

For teams that want outside help hardening a prototype, JAMD Technologies offers a Vibe Code Finisher service along with private, self-hosted AI, built around a security-first approach, custom builds and long-term support.

Habits That Keep Vibe Coding Productive

Most doom loops can be avoided with a few simple habits:

• Work in small, testable steps. Ask for one feature at a time and check it before moving on.
• Save working versions. Commit to Git, or use your tool’s version history, every time something works, so a bad change is one click from undone.
• Write a short spec before prompting. Even five bullet points on users, inputs, rules and outputs will sharpen everything the AI produces.
• Start fresh when it goes in circles. Open a new session with a clear summary of what the app does, what works and the one thing you need fixed. A clean context often solves what twenty more prompts won’t.
• Ask the AI to explain its code and list its assumptions. Wrong assumptions are much easier to catch in a list than in a crash.
• Ask it to write tests, then run them after every change.
• Keep real customer data out of prompts. Use made-up sample data that has the same shape.
• Know when to stop. If the same bug survives three or four attempts, step back and get a person who can read the code to look at it.

If you’re trying vibe coding for the first time, pick one small, annoying task you do every week, write a one-sentence problem statement for it, and build that. You’ll learn more from one finished small tool than from a dozen half-built big ones.