What is Vibe Coding?

Vibe coding is an AI-assisted way to code using plain language prompts, allowing developers to build software faster and think more freely. Learn how vibe coding works and how to use it.

The meaning of vibe coding

Vibe coding is defined as a software development practice in which people use AI to generate code rather than doing it manually. Instead of writing code line by line, people “vibe” with an AI assistant that creates, refines, and even deploys the code for them based on their intent.

Andrej Karpathy, a well-known computer scientist and AI researcher, coined the term in early 2025. Since then, it has rapidly gained popularity to describe a way of coding that allows you to:

• Describe ideas in plain language.

• Iterate quickly without breaking your flow.

• Refine your code structure based on the initial output.

Vibe coding is part of a broader shift in AI in software development that makes coding more accessible, expressive, and collaborative. For this reason, it’s particularly well suited for early-stage projects and creative exploration. It also helps make software development easier for people without deep knowledge of programming languages.

Types of vibe coding tools include simple AI code completion assistants that respond directly to prompts, and advanced agentic AI systems that can iterate on code on their own to reach a predetermined goal.

However, vibe coding doesn’t replace development skills. Developer experience and judgment, knowledge of how code works, and an understanding of the precepts of computer science are all important in shaping AI outputs.

Key takeaways

• Vibe coding uses AI and natural language prompts to help developers generate, refine, and test code faster.

• Effective vibe coding still depends on human oversight, coding knowledge, and careful review to help ensure software quality, security, and maintainability.

• Strong prompting practices, iterative refinement, and continuous testing help developers get more accurate, useful, and maintainable AI-generated code.

• Vibe coding can reduce repetitive work, speed up experimentation, and make software development more accessible to experienced developers and beginners alike.

• Different types of vibe coding tools—from AI coding assistants to advanced agentic AI systems—support different workflows, skill levels, and development goals.

Why vibe coding matters now

Vibe coding matters now because developer teams face growing complexity, faster release cycles, and persistent skill gaps. Vibe coding shifts focus from rigid processes to intuitive, flow-based collaboration, helping developers move quickly without losing clarity. By supporting modern workflows, it reduces friction in the pipeline, improves momentum, and supports consistent delivery in high-pressure environments.

Vibe coding vs. traditional coding

Vibe coding and traditional coding reflect two distinct approaches to software development, each with tradeoffs. Vibe coding prioritizes flow, intuition, and rapid iteration, while traditional coding focuses on strict planning, defined workflows, and process control.

At a glance: Vibe coding vs. traditional coding

How vibe coding works

Start with a natural language prompt that clearly tells an AI tool such as GitHub Copilot what you want it to do. The tool will respond with a code suggestion based on your intent. This process is an example of AI code generation, where models interpret natural language and produce executable code.

Here are some example prompts that you might use with Copilot:

• Build a responsive HTML page for a music app with a call-to-action button.

• Plot the top five countries by population using Pandas and Matplotlib libraries.

• Generate an SQL query that filters customer orders by date and total amount.

You review the output, adjust the logic, or add new instructions to shape the output into working code while you stay in the flow of your project. Often, you can quickly develop a working prototype using agentic systems that support vibe coding. Vibe coding creates a conversational workflow that allows you to refine as you go.

Different approaches to vibe coding

You can approach vibe coding in two distinct ways: full vibe coding and human-led vibe coding. Depending on which approach you choose, expect tradeoffs between speed and oversight.

Full vibe coding

With this approach, you fully trust the AI output and minimize or altogether skip reading and testing the code. Your focus is on thinking of an idea, instructing the AI tool to bring it to life, and using the output. You’re not overly concerned with how the tool implements your instructions, making this approach best suited for running quick experiments or creating impromptu prototypes.

Human-led vibe coding

With this approach, you stay in control and treat the AI tool as an assistant, a collaborator or, as sometimes described, an AI pair programmer. You guide the process with clear prompts then actively review, test, and refine the output. Tools such as Copilot support human-led vibe coding by emphasizing responsible development, where you take the time to understand and own the code. This approach demands more of your time but is critical to helping ensure software quality and project accountability.

How to get started with vibe coding

Vibe coding comprises a repeatable sequence of steps that makes it easier to dive into projects and keep them moving forward. Once you’ve chosen an AI coding tool, the process is simple: describe what you want, see what the AI tool suggests, then shape the result until it fits your objectives. It’s a flexible way to build, ideal for moments when sketching in code feels faster than planning everything out. This approach works with a variety of AI coding tools for beginner and expert coders.

1. Choose a tool that fits your flow

Look for a tool that works with your preferred technology stack and setup. Copilot works with popular editors such as Microsoft Visual Studio Code and JetBrains, offering natural language input and context-aware suggestions.

For example, if you’re building a data visualization app in Python, you can ask Copilot to suggest libraries, write sample plots, and explain what each block of code is doing.

2. Describe what you want to build

Use plain natural language prompts to explain your goal. You’ll get the best code when you craft quality prompts and provide good context.

Here are a few tips for writing prompts:

• Mention inputs, outputs, or constraints.

• Include visual or style preferences.

• Keep requests short and focused.

Here’s a short, to-the-point sample prompt: “Create a Flask app with one endpoint that checks if a number is prime. Include error handling.”

In addition to writing quality prompts, provide your AI tool with a product requirements document that gives it more context. Watch a short video describing what a product requirements document is and how it can help you get the most out of vibe coding.

3. Shape the output

Treat the response like a rough draft. Refine and adapt the output as you go, including requesting that the tool run tests to help validate the results.

You might ask the tool to perform the following actions:

• Break up long blocks into smaller functions.

• Rename variables for clarity.

• Clean up, simplify, or extend a section.

• Run unit or edge case tests to catch issues early.

4. Check before you move on

Test the code and make sure it aligns with your original intent.

Here are recommended final steps:

• Run unit tests (a key part of software testing).

• Review dependencies.

• Clean up your code with a linter or formatter.

5. Keep the rhythm going

When paired with effective AI code reviews, vibe coding makes it easier to explore, build, and ship without getting stuck in setup. Return to the rhythm of vibe coding anytime you’re ready to create.

Prompt engineering best practices

Follow these best practices for prompt engineering when using tools such as Copilot:

Start with a clear goal.

Once you’ve described your high-level objective then layer in specifics, including the language and framework, inputs and outputs, and any performance or style requirements. This reduces ambiguity and leads to more relevant code.

Provide context wherever possible.

Reference existing files, patterns, or APIs so the model can align with your codebase. If you want a specific structure or output format, provide a short example. Even a small input/output pair or function signature can guide the response significantly.

Break complex tasks into smaller steps.

Don’t request the AI tool to do too much too soon. For example, rather than ask for a complete feature in one prompt, use iterative prompting—plan, generate, test, refine. This approach will produce more reliable results and make debugging easier.

Be explicit about constraints.

These can include naming conventions, error handling, edge cases, or what not to modify. The more guardrails you set, the less cleanup you’ll need to do later.

Why vibe coding works

Vibe coding turns coding into a conversation, making it easier to explore and share, without overthinking the code. Here are some benefits you’ll notice right away:

Accelerates time from idea to working feature or product.

Plain language prompts make it simple to sketch out concepts and build early versions fast.

Example: A solo developer builds a functional crypto dashboard in under an hour, requesting charts, API calls, and styling within the flow of her work.

Reduces barriers to entry.

You don’t need to master syntax to start building. Describe what you want and see it come to life.

Example: A design student builds a portfolio site using prompts such as Center this div and Make the background black.

Frees you to focus.

With AI handling boilerplate, you spend less time on repetitive tasks and more time thinking through code logic and architecture and user needs.

Example: A team uses Copilot to generate unit tests and form validation, freeing up time for product thinking.

Transforms coding into a conversation.

Vibe coding makes iteration feel more natural. You describe, adjust, and improve, one prompt at a time.

Example: A developer highlights a slow function and asks the AI to optimize it, then reviews the result before moving on.

Keeps you in control

AI lends speed and flexibility to the coding process, but you’re still the one shaping the outcome.

What are the limitations of vibe coding?

Software development gains momentum with vibe coding. However, when used without care, vibe coding can introduce issues that slow down progress or make your project harder to manage.

Technical complexity

AI tools are great at helping you accomplish common tasks, but they often miss the details in more advanced projects. They tend to assume everything will go smoothly so skip over threading, performance, or error handling.

Be sure to pay extra attention when your project involves any of the following:

• Complex workflows

• Real-time systems or hardware integrations

• Logic that needs tuning or precision

Quality and consistency

Code that runs isn’t always ready for production. Without close attention, it might be clunky, repetitive, or hard to maintain.

When vibe coding, watch out for:

• Redundant logic

• Inefficient solutions

• Disorganized structure or formatting

Debugging and clarity

It’s often unclear why an AI assistant makes certain decisions. That lack of visibility can slow you down, especially when you’re trying to understand, fix, or extend generated code.

Here are some common trouble spots:

• Vague variable names

• Missing documentation

• Hard-to-follow logic

Long-term maintenance

Vibe coding is great for quick progress, but if you skip cleanup, things get messy. Inconsistent naming, scattered logic, or untracked dependencies can pile up fast, contributing to technical debt. This becomes especially risky with an influx of code or micro apps built outside established review or DevSecOps practices.

Follow these best practices to help ensure long-term code quality and maintainability:

• Review and refactor early.

• Add comments and documentation.

• Stick to your team’s standards.

Security risks

Unless prompted, your AI tool might overlook safeguards or repeat risky patterns without warning.

Common risks include the following:

• Hardcoded credentials

• Insecure forms or endpoints

• Unvalidated user input

Human involvement

Vibe coding can speed development, but you must stay in control of the process. Vibe code responsibly with Copilot by treating it as an assistant or a collaborator. Guide it with clear prompts and then review, test, and shape the output to help ensure the code is accurate, secure, and aligned with your goals.

The role of AI in vibe coding

These tools translate your intent into code that adapts with each new prompt. AI tools designed for vibe coding rely on large language models trained on public code and natural language prompts—a method informed by breakthroughs in AI models and natural language processing.

What AI tools can do:

• Turn natural language into code

• Handle setup, boilerplate, and formatting

• Suggest next steps or full functions

• Explain logic or clean it up

• Adjust to changes mid-task

How AI drives the rhythm of vibe coding

Vibe coding uses AI to create a rhythmic, back-and-forth exchange conducive to exploring ideas and refining structure and intent.

Here’s the conversational loop at the heart of vibe coding:

1. Describe your goal

2. Let the AI generate a draft

3. Request revisions or add constraints.

4. Repeat until the output is where you want it.

Before you know it, you’ve turned your prompts into real, usable output.

The human role in vibe coding: Creativity and oversight

Vibe coding would be impossible without AI, but human creativity is just as vital. People bring their intuition, originality, and emotional insights to the process in a way that algorithms alone cannot replicate. Only people can create meaningful user experiences that feel intentional, human-centered, and genuinely useful.

Human oversight is also critical. Responsible vibe coding practices require that humans stay in the loop, collaborating with the AI tool to shape and review every stage of output.

Watch out for these common pitfalls when vibe coding:

• Over-reliance on AI. Don’t accept generated code at face value. It can’t be trusted to always be secure, efficient, or correct.

• Skill shortcuts. Beginners might be tempted to skip learning the fundamentals of coding, but doing so will affect their ability to effectively review output.

• Lack of clarity. AI can’t explain its choices, which makes it harder to trace decisions or spot edge cases.

Remember, vibe coding is a creative, collaborative process. You bring the ideas, the AI tool responds. You stay in charge, and you determine the result.

How to use GitHub Copilot for vibe coding

Vibe coding is reshaping how people build software, prompt by prompt. People with a range of backgrounds use it to test ideas, collaborate more easily, and stay focused.

Here’s how to vibe code step by step with Copilot, which uses machine learning models to support dynamic, iterative development workflows:

Step 1: Define your goal.

Begin by using the Github Copilot Chat feature to describe in plain language what you want to build. Ask a high-level question or define the intended feature, and then chat with Copilot to clarify requirements and outline the solution before coding. Example prompt: Build a REST API endpoint for user authentication using Node.js and Expr.

Step 2: Start coding.

Copilot provides real-time inline code suggestions as you type—from single lines to entire functions—directly inside your IDE. Copilot will suggest code based on context and patterns, generating full functions or boilerplate.

Example prompt: Create an Express server with a login route.

Step 3: Iterate on the AI-generated code.

Use Chat (and optionally slash commands) to fill gaps quickly. Ask for specific additions, such as validation or error handling. You can also ask more general questions, such as to explain code, concepts, or development tasks. Keep this development stage iterative and conversational.

Example prompt: Add input validation and error handling to this login route.

Rather than working in the IDE, you can use GitHub Copilot CLI for environment setup or scripting. Use natural language prompts to create working shell commands and scripts.

Example prompt: Create a script to run the server and check for errors.

Step 4: Scale across files.

Reference related files or modules or the full project, then ask Copilot how these various pieces connect. Copilot will use this wider context to provide better suggestions, help you maintain consistency across files, and clean up code through refactoring.

Example prompt: @workspace how does authentication logic flow across this project?

Step 5: Accelerate development with agents.

Delegate more complex, multi-step tasks through AI agent orchestration, using Copilot autonomous agents within multi-agent systems that can run in the background. For example, you can ask it to analyze a repository and plan work, make code changes across files, and automatically open pull requests.

Example prompt: Add JWT authentication to this project, including middleware and tests.

Step 6: Validate and refine code.

Use the code review feature to ask Copilot to highlight any risks, bugs, and improvements.

Example prompt: Review this code and identify quality, performance, and security issues.

Step 7: Generate tests and documentation.

Before shipping, use Copilot to automatically create test suites and documentation. You can be assured that Copilot uses best practice techniques for code documentation.

Example prompt: Generate unit tests for the login endpoint, including edge cases.

Step 8: Prepare the code for team collaboration and shipping.

Copilot supports the pull request workflow by generating summaries, suggesting code review comments, and tracking changes.

Example prompt: Summarize the key changes in this pull request and highlight areas for reviewers.

Types of vibe coding tools

AI tools designed to help you build software with minimal coding can run inside a standalone code editor, an integrated development environment (IDE) with a built-in code editor, or a browser. AI agents can work inside your IDE, in the browser, or across multiple tools, taking multistep actions based on your goals.

Here are the main types of tools used for vibe coding:

AI code completion assistants

These tools work inside your editor to suggest code as you type. They’re great for boosting productivity by handling routine tasks, such as boilerplate or repetitive logic.

Conversational AI coding assistants

These tools let you describe what you want in plain language. They help you interact with your code, understand it in context, and learn as you go. Use them for prototyping and idea exploration, debugging or reverse-engineering unfamiliar code, solo projects, and hands-on learning.

Agentic coding tools

These tools follow prompts to generate full app scaffolds that you can use as working starting points. They also help you quickly create multi-agent systems, which can perform multi-step tasks automatically. With agentic coding tools, you’re free to experiment and see what’s possible.

Think of multi-agent systems as one layer in a hierarchy of functional layers that comprise AI systems, workflows, and apps. The AI orchestration layer is the coordination layer that connects multiple AI components—models, agents, tools, APIs, and data—into a single workflow. This layer depends on the Model Context Protocol (MCP) and other standards to coordinate actions across systems. The AI agent orchestration layer builds on AI orchestration by acting as the control layer that coordinates the work of multiple AI agents. Next comes the multi agent systems, which make up the interaction layer that defines how agents within the system behave.

AI-native IDEs

These are development environments built around AI from the ground up, not just augmented with AI-powered features. They often integrate code generation, editing, testing, and debugging functions with broader development processes into a single AI-driven workflow. They’re optimal for allowing you to quickly develop working software through AI-assisted collaboration, especially when you want to maintain control over the codebase. Not all AI-native IDEs can be considered vibe coding platforms, but many are increasingly adding agents and other vibe coding tools.

AI app builders

Many AI app builders are vibe coding platforms that let you create and even ship end-to-end applications with minimal manual coding. Working through conversational or prompt-based workflows, they can assemble a full app, including the user interface, logic, and connections. They’re useful for rapidly turning ideas into working apps, especially for internal tools, prototypes, or workflows that need to connect to data and services.

Choose what fits

Pick the vibe coding tool that suits your stack, style, and goals. If you want to use vibe coding to become a developer, start with an AI assistant such as Copilot. Or if you’re already an expert developer, see how working in an IDE such as GitHub Copilot Codespace can help you write code faster, with less effort, and free you to focus on problem solving and collaboration.

Get started with a vibe coding tutorial for Copilot.

At a glance: Types of vibe coding tools with examples

How to apply vibe coding to software development

Traditional software development is a systematic process of designing, coding, testing, and maintaining software applications and systems. Vibe coding offers a more natural way to design, build, and adapt software. By using plain language and AI assistance, you can sketch ideas, shape code as you go, and stay focused on what matters. Vibe coding is a flexible approach that fits into modern workflows and adapts to different team needs.

Here are some ways that vibe coding enhances software development:

Go from idea to prototype quickly.

Developers can use prompts to sketch and test ideas before choosing a tech stack or setting up architecture.

Example: A startup team uses Copilot to spin up user interface components and backend routes for a scheduling app. Within hours, they’re demoing a working prototype to stakeholders. Go from prototype to shipping quickly. Developers can carry a prototype through to production by iterating with prompts that save time at each stage in the development lifecycle. This can include refining features, aligning the technology stack, testing and debugging, and optimizing for performance.

Example: The same team iterates on its prototype for a scheduling app by relying on Copilot to shape the prototype to business needs and launch it. In a short cycle, the prototype becomes a maintainable codebase—deployed, in use, and generating real user feedback.

Cut boilerplate and move faster.

Vibe coding handles repetitive tasks such as form validation and API scaffolding, so developers can focus on logic and structure.

Example: An enterprise developer uses Copilot to generate unit tests and scaffold routes—cutting build time and reducing context switching.

Support learning and onboarding.

New team members can explore unfamiliar code by asking questions and generating simplified examples.

Example: A junior developer working on a web app uses Copilot to improve a code block. Copilot suggests a cleaner, more idiomatic JavaScript version, helping him understand best practices as he refines the code.

Collaborate across roles.

Non-engineers can describe intent in plain English and shape code that developers then refine.

Example: A designer shares layout details in natural language to generate responsive HTML/CSS for a landing page. A developer then reviews and adjusts the code for consistency and accessibility.

Stay flexible mid-project.

Rather than start over, developers can take an existing project in a new direction by asking the AI tool to adjust the code.

Example: A development team reworks its architecture mid-project by prompting for updated scaffolds and logic adjustments—speeding up iteration without losing momentum.

Conclusion

Vibe coding brings a new level of speed and accessibility to software development by using AI to help translate ideas into code. It can lower the barrier to entry for beginners and help experienced developers move faster from concept to prototype to production. In all cases, it delivers the best results when human creativity, judgment, and reviews stay firmly in the loop.

(1) Jonathan Yu, “AI-Powered Unity Game Development with GitHub Copilot: Long Term Review,” last modified November 13, 2023.

(2) Bryan Puryear and Greg Sprint, “GitHub Copilot in the Classroom: Learning to Code with AI Assistance,” Journal of Computing Sciences in Colleges 38, no. 1 (2022): 37–47