What Is Vibe Coding?
A natural-language-first way of building real software — and the testing, security review and human judgment it still requires before anything ships.
Natural-language software development
Vibe coding means describing the software you want in plain English and letting an AI tool produce the actual code, rather than writing every line yourself. You work by outcome — "add a pricing page with three tiers" — instead of by implementation detail, and you keep steering the result with more plain-English instructions rather than editing syntax directly.
It's not "no-code" — real, readable, version-controlled code is still being produced underneath, in a real framework, and it still has to run correctly, get committed to Git, and get deployed like any other codebase. What's different is the interface you use to produce it. For the broader category this sits inside, see what AI coding means more generally.
Prompt-driven development
In vibe coding, the prompt is your primary tool — the quality of what you get back is largely a function of how specific and concrete your prompt is. "Make a login page" produces something generic; "add a login page with email and password fields, a 'forgot password' link, and inline validation errors under each field" produces something you can actually use.
You don't have to write every prompt from scratch — the prompt library has working, specific prompts organized by task, which is a faster way to learn what a good prompt looks like than guessing on your own.
Iterative development: the vibe coding loop
- 1
Describe
You explain what you want in plain English — a feature, a page, a fix — the way you'd brief a colleague, not the way you'd write code.
- 2
Generate
The AI produces real, working code in a real framework, based on your description and whatever context it has of your project.
- 3
Run
You actually run it — the dev server, the test suite, the feature itself — rather than judging it by how the code looks on the page.
- 4
Review
You read what changed. This step is the difference between vibe coding that works and vibe coding that quietly breaks production.
- 5
Refine
You describe what's still wrong, precisely, and repeat the loop — narrowing in on a correct result rather than accepting the first draft.
Almost nothing worth shipping comes out right on the first pass. The loop above, run three or four times against the same feature, produces dramatically better results than one long, detailed prompt followed by silent acceptance of whatever comes back.
AI debugging
Debugging inside a vibe coding workflow still starts with you: reproduce the problem, and describe the exact symptom, error message, and failing input to the AI rather than a vague "it's broken." The AI proposes a fix; you're still the one who verifies it actually resolves the original case before accepting it.
Read the full debugging tutorialAI-assisted testing
AI tools can write test cases quickly, but they don't know what "correct" means for your specific business logic — only you can define that. Ask for tests explicitly, then read them: a test that merely confirms the code does what it currently does isn't verifying anything useful.
Find a testing promptHuman review still comes before anything ships
This is the part of vibe coding that's easiest to skip when a demo looks finished — and the most important not to.
AI-generated code that runs without errors is not the same thing as AI-generated code that's correct, secure, and ready for real users. A model can produce confident, plausible-looking code that silently mishandles an edge case, skips an authorization check, or leaves a credential exposed — and it will never flag its own uncertainty unless you specifically ask it to check.
Before anything built this way reaches real users, work through a real checklist, not a vibe:
- No secrets, API keys, or credentials hardcoded anywhere in the generated code
- User input is validated and sanitized before it touches a database or another service
- Authentication and authorization checks are actually present on every route that needs them, not just the obvious ones
- Edge cases are handled — empty input, failed network requests, unexpected types — not just the happy path shown in the demo
- Tests exist for the behavior that matters, and you've verified they actually fail without the fix
- You understand what the code does well enough to explain it to someone else, not just that it appears to work
Deployment
Once a feature has actually been tested, reviewed and validated — not just "looks done" — it goes through the same deployment process as any other software: committed to Git, pushed, built, and released to a real environment with real monitoring.
The deployment tutorial walks through taking a project from your machine to a live URL, including handling environment variables correctly — a step AI tools frequently get wrong by default.
Questions people ask about vibe coding
Is vibe coding safe to use for a production application?
Only if you treat the AI's output the way you'd treat a first draft from any contributor — tested, reviewed for security issues, and validated against real requirements before it ships. Vibe coding describes how the code gets written, not whether it's automatically safe to deploy.
Do I need to learn to code to vibe code?
You can produce working software without deep coding knowledge, but you'll hit a ceiling quickly if you can't read the code well enough to catch mistakes, debug a failure, or judge whether a security concern applies to your app.
What's the difference between vibe coding and AI coding?
Vibe coding is a specific style of AI coding — describing outcomes in natural language and iterating conversationally. AI coding is the broader category, which also includes things like inline autocomplete inside a traditional, line-by-line workflow. See /ai-coding for the fuller picture.
Can vibe coding replace hiring a developer?
For a small personal project or an internal prototype, often yes. For anything handling real user data, payments, or a business's reputation, a lack of technical review is the actual risk — not the AI's code quality on its own.
Why does the AI's code look confident even when it's wrong?
Language models generate the most statistically plausible next piece of code based on patterns they've seen, not by verifying correctness against your specific requirements. Plausible-looking and correct are not the same thing, which is exactly why review is a non-negotiable step, not an optional one.
Try the loop on something real
Pick a tutorial with real, working code, or grab a specific prompt from the library and start iterating.