What Is Vibe Coding? A 2025 Guide to Prompt-Driven Development with AI
What Is Vibe Coding? A 2025 Guide to Prompt-Driven Development with AI
Vibe coding is an AI-driven software development approach where developers describe the desired outcome in natural language prompts rather than writing code or prompting for code. The AI application generates working code in response. However, this code isn’t production-ready and requires integrations, validations, and testing.
Sharon Barr
Co-founder & CEO @ Early | Agentic AI Test Engineer
June 4, 2025
In 2025, a new paradigm of software development is emerging (and exploding!). Traditional coding is taking a back seat to conversational, prompt-first workflows. Known as vibe coding, this AI-powered approach lets developers describe what they want to an AI agent or chatbot, and get working code in return at unprecedented speed. But vibe coding comes with its own set of quality risks, which we believe will lead to vibe test coding exploding as well in the upcoming decade.
In this guide, we'll unpack what vibe coding means, how it works, when to use it, and how to overcome its quality challenges with vibe coding-driven testing. If you’re a developer, team lead, or group leader, or engineering manager, this article will help you understand the changes that are happening in development and how you can use AI in the upcoming decade to become a better developer.
But first, let’s start at the beginning, and look at the time code was born from cogs, vacuum tubes, and a few punch cards.
Table of contents:
Picture London in 1843. A twenty-seven-year-old mathematician named Ada Lovelace (the software language Ada is named after her) leans over a notebook lit by gas-lamp glow, scribbling a sequence of instructions for Charles Babbage’s never-finished Analytical Engine. She isn’t describing gears or cogs; she is describing processes. With that act she plants the first seed of software: a routine that, if the Engine were ever built, would calculate Bernoulli numbers all by itself.
Fast-forward a century. War has driven science forward at terrifying speed, and in the basement of Manchester University engineers slide a paper tape into a refrigerator-sized box that clicks, whirs, and eventually lights a single neon lamp: “✓”. The Manchester Baby has just executed the world’s first stored program. It takes hours to flip vacuum-tube bits, but for the first time a machine can remember what to do next.
By the 1950s programmers became tired of punching binary holes. Enter FORTRAN,a language that lets scientists write “X = A * B + C” instead of “011101.” Suddenly code feels less like engineering a bridge and more like writing a sentence. The community explodes; COBOL, Lisp, C, and a parade of dialects follow. Each new abstraction widens the circle of people who can instruct machines, and with every widening the volume of code doubles, then doubles again.
The 1990s then brought the Web, open-source licenses, and cheap PCs. A teenager in Helsinki publishes Linux; another in Illinois uploads the first pull request to something called “Git.” Code becomes communal, remixable, borderless. By the time GitHub turns ten, humanity has written trillions of lines, running banks, planes, pacemakers, and the phone that may be buzzing in your pocket right now.
Then came AI developer workflows in 2022/3, allowing for coding with LLMs. Ask for a Python script that reads a CSV file containing user data (name, email, age), validates that email is in the correct format and age is a number over 18, then outputs the data to a JSON file.“ The AI outputs a complete, ready-to-run Python script. Anthropic’s CEO predicts that 90% of code will be written by AI as early as this year. At this pace we’ll write more code in a year than in the entire 20th century, trading punch cards and compilers for conversation.
And now, in 2025 and onwards, we’ve arrived at the stage of "vibe coding“.
“Vibe coding” is the nickname Andrej Karpathy gave, in a February 2025 post, to the moment a developer stops writing code and starts describing solutions and requirements.
Imagine telling an assistant: “Spin up a dark-mode React dashboard with Stripe checkout.” Seconds later you’re staring at a working UI, server endpoints, and a tidy Jest suite. That conversational, prompt-first workflow is what developers now call vibe coding. It’s GenAI for developers on speed-dial.
How do developers use vibe coding in 2025? The main use cases for vibe coding today are prototyping, demos, and occasionally lightweight MVPs. This is because vibe coding allows developers to rapidly translate ideas into working code without getting bogged down in syntax or boilerplate, so they can accelerate the SDLC process.
Here’s a step-by-step flow of how developers perform vibe coding:
- Intent and Prompt: The developer describes the required feature to the AI application with prompt-driven development and includes any mock-ups or DB schemas needed. There are many AI coding tools and applications that support this. For example, agents like GitHub Copilot, Google’s Jules, Cursor, Windsurf, and chat-only builders like Lovable, or Bolt.new.
- Generative burst: The AI application such as drafts code, installs libraries, and wires well-known services like Firebase Auth, Stripe payments, or SendGrid emails. The AI application knows how to use them since it has seen these services thousands of times during training.
- Chatty refinement/ Prompt engineering: The developer replies “Paginate results,” “Add dark mode,” etc. and patches only what’s needed.
- Basic self-test loop: The agent writes basic low-or-medium-complexity unit/integration tests and runs them. In the future, it may suggest fixes, or even apply them autonomously.
“Vibe coding” turns a short prompt into a working feature. However, there are three practical catches today introducing challenges in AI-generated code. They keep us from plugging the deliverable straight into every production stack:
1. The Integration catch – private APIs
Vibe tools know how to talk to Firebase, Stripe, or SendGrid because those signatures saturate their training data. Your internal AuthService or OrderAPI, however, sits behind a firewall and is named in ways the model has never seen. Without a formal spec (OpenAPI, GraphQL, gRPC) the agent can only guess, so it wires a generic placeholder instead of the real thing.
2. Maintainability catch – long-term health
Generated code often compiles, passes a quick test, and looks tidy. However, it may wander from team conventions, duplicate helpers, ignore architectural boundaries, or bury TODOs. Unless style guides, linters, and refactor bots feed back into the prompt loop, yesterday’s “instant scaffold” can become tomorrow’s technical debt and maintenance nightmare.
3. Quality catch – hidden correctness gaps.
The model’s first draft might overlook edge-case validation, performance hot spots, or subtle race conditions. Today’s test generators cover mainly the obvious happy paths; deeper property checks and load scenarios still rely on human engineers. Until auto-generated test suites mature, reviewers must stay vigilant to catch silent faults before they ship.
Collectively, these catches mean vibe coding is already a productivity leap, but it still needs clear API contracts, enforced style policies, and robust tests to guarantee that the shiny new code stays integrated, maintainable, and rock-solid in production.
Those catches lead directly to the test explosion we’ll explore next.
By the late-2020s, analysts expect AI assistants to pour out hundreds of billions of lines a year, and the majority of them will arrive pre-written by machines. Industry leaders now project that AI will write 90% of new code as early as 2027.
What often gets missed in that headline is how much of this tidal wave will not be application logic code, but rather test code. This is especially true when it comes to code that finds itself in production systems.
Here’s what I predict will happen to test code created through vibe coding.
- Today: Mature teams hover around 20–40% test code to application code ratio. That’s already a big jump from the single-digit ratios of the 2010s, which was driven by CI mandates, unit-test cultures, and automated software development.
- Mid-decade: As LLM agents mature, every new function will be born with a swarm of generated assertions, and unit, property, fuzz, and load tests. The balance could tip to 60–70% test code to application, by 2027.
- End-decade: If “test-all-the-things” agents reach full coverage and regression guardrails become hands-free, the ratio could reach a 9-to-1 split, i.e, up to 90% of a codebase devoted to verification. Paradoxically, lines of code will keep climbing even faster, because each new feature drags a larger shadow of test artefacts behind it.
In other words, the supernova isn’t just more features exploding into existence; it’s an even larger halo of automated safety checks forming around them. This will ensure that velocity doesn’t outrun reliability.
- The end result: By 2030 an average repo may hold 10 to 100 times more test code than business logic.
What does this mean for developers, enterprises and testing teams? In Part 2 we’ll look at the guardrails, the infrastructure, and the new skills developers will need to keep this super-charged future on the rails.
What is vibe coding?
Vibe coding is an AI-driven software development approach where developers describe the desired outcome in natural language prompts. The AI agent or chatbot generates code in response.
How is vibe coding different from traditional coding?
Traditional coding requires developers to manually write every line of logic. In vibe coding, developers guide the process through prompts, allowing AI to generate, test, and refine code automatically. This is used especially for prototyping and MVPs.
Is vibe coding suitable for production environments?
Vibe coding is great for rapid prototyping and experimentation. But production use still requires strong guardrails such as formal APIs, testing coverage, and code quality enforcement to ensure reliability and maintainability.
What are the risks of vibe coding?
Major risks include poor integration with internal APIs, inconsistent code quality, and insufficient test coverage. Without human review, generated code might miss edge cases or introduce long-term maintenance issues.
Will vibe coding replace traditional developers?
Vibe coding enhances developer productivity but doesn’t replace problem-solving, architectural decisions, or nuanced debugging. Developers will shift from writing boilerplate to guiding AI and validating output.
What is the future of test code in AI-driven development?
As AI generates more application logic, the volume of test code will skyrocket to ensure quality. This makes automated testing a critical skill for developers in the upcoming decade.