What is Vibe Coding and How Can You Get Started With It?

Vibe coding was first coined in February 2025, by Andrej Karpathy, cofounder of OpenAI and former AI lead. The term was used to describe a new way of building software where you give AI instructions in simple language and it writes the code for you.

Karpathy described the approach as fully giving in to the AI, embracing what exponential model progress makes possible, and letting go of the need to read or understand the code. 

This makes it possible for non technical people to code software applications and platforms too. You don't need to know what every line means or understand the syntax. You just describe what you want and the AI builds it for you.

Why did this shift happen? Large language models crossed a quality threshold where they started to produce working code from natural language. 

Wikipedia defines vibe coding as "an AI assisted software development practice where the developer describes a project or task to a large language model, which generates source code based on the prompt." 

Most people using vibe coding are not software professionals. It opens up new avenues for everyone, whether inside the tech space or not.

The vibe coding workflow comes down to four steps that repeat until your application is done.

First, you start by writing a prompt. This is a normal description of what you want to build. Something like "build a dashboard that shows weekly revenue by product" or "create a contact form that stores responses in a spreadsheet." The clearer your description, the better the output of the AI is.

Code is generated based on your prompt. Depending on the tool you use, it might produce the frontend, the backend, or a full stack application ready to run.

The next step is to review and assess. You don't need to understand every line. But you need to check if it does what you asked. Anything that looks wrong or works wrong needs to be fixed. Run it, test it, and look for obvious problems. 

The final step is to iterate. If something's off, write another prompt to fix it. Vibe coding is entirely conversational. You build the application through a series of prompts and adjustments.

Relying on natural language as the primary interface is what separates this from older code generation tools. The core skill here is not programming. It's being specific and conversational. Clear prompts produce usable results.

AI models have reached a point where they can produce working, deployable code from natural language. 

That quality threshold wasn't met a few years ago. Now that it has, adoption has moved very fast.

84% of developers worldwide use or plan to use AI coding tools. GitHub Copilot has surpassed 20 million users and is growing more than ever. The global vibe coding market sits at $4.7 billion and is growing at over 27% annually.

IBM notes in its vibe coding overview that for enterprise teams, the ability to prototype and validate faster has a direct impact on competitive positioning. That's why adoption is quicker and visible than ever.

For startups, vibe coding is changing how they’re built and this story is playing out across every startup field right now. Founders who previously needed a dedicated developer to test an idea can now prototype and validate on their own.

Everything has its advantages and disadvantages. Here are the pros and cons:

The Pros of Vibe Coding

The biggest advantage is faster build times. What used to take a developer a week can take hours with the right tool and a clear prompt. Non developers can ship products too. The barrier is low enough that founders and product managers can test ideas without needing a full engineering team behind them. And costs go down because you need fewer people to produce the same output.

The Cons of Vibe Coding

Security is a real concern. Apps built on popular vibe coding platforms can have severe security flaws in their access controls. AI tools optimise for speed, not safety, by default. Code quality is also inconsistent. AI generated code produces more issues than human written code. And if you ship code you haven't reviewed, you're building on an unstable foundation.

Ensure you review the output before it goes into production. Understanding what each type of AI agent does and when to use it gives you a clearer picture of everything.

For teams working with advanced AI infrastructure, pairing vibe coding workflows with capable models like DeepSeek can highly improve output quality on complex builds.