What is vibe coding?

Vibe coding is an emerging software development practice that uses artificial intelligence (AI) to generate functional code from natural language prompts, accelerating development, and making app building more accessible, especially for those with limited programming experience.

The term, coined by AI researcher Andrej Karpathy in early 2025, describes a workflow where the primary role shifts from writing code line-by-line to guiding an AI assistant to generate, refine, and debug an application through a more conversational process. This frees you up to think about the big picture, or the main goal of your app, while the AI handles writing the actual code.

In practice, vibe coding is generally applied in two main ways:

"Pure" vibe coding: In its most exploratory form, a user might fully trust the AI's output to work as intended. As Karpathy framed it, this is akin to "forgetting that the code even exists," making it best suited for rapid ideation or what he called "throwaway weekend projects," where speed is the primary goal.
Responsible AI-assisted development: This is the practical and professional application of the concept. In this model, AI tools act as a powerful collaborator or "pair programmer." The user guides the AI but then reviews, tests, and understands the code it generates, taking full ownership of the final product.

Understanding how the vibe coding process works

Vibe coding operates on two levels: the low-level iterative loop of refining code, and the high-level lifecycle of building and deploying a full application.

The code-level workflow

This is the tight, conversational loop you use to create and perfect a specific piece of code.

Describe the goal: You start with a high-level prompt in plain language. For example: "Create a Python function that reads a CSV file."
AI generates code: The AI assistant interprets your request and produces the initial code.
Execute and observe: You run the generated code to see if it works as intended.
Provide feedback and refine: If the output isn’t quite right or an error occurs, you provide new instructions, like, "That works, but add error handling for when the file is not found."
Repeat: This loop of describing, generating, testing, and refining continues until the code is complete.

Describe the goal: You start with a high-level prompt in plain language. For example: "Create a Python function that reads a CSV file."
AI generates code: The AI assistant interprets your request and produces the initial code.
Execute and observe: You run the generated code to see if it works as intended.
Provide feedback and refine: If the output isn’t quite right or an error occurs, you provide new instructions, like, "That works, but add error handling for when the file is not found."
Repeat: This loop of describing, generating, testing, and refining continues until the code is complete.

The application lifecycle

This is the broader process of taking a high-level idea from concept to a deployed application.

Ideation: You describe the entire application you want in a single, high-level prompt in tools like Google AI Studio or Firebase Studio.
Generation: The AI generates the initial version of the full application, including the UI, backend logic, and file structure.
Iterative refinement: You test the application and use follow-up prompts to add new features or change existing ones.
Testing and validation: A human expert reviews the application for security, quality, and correctness.
Deployment: With a final prompt or a single click, you deploy the application to a scalable platform like Cloud Run.

Ideation: You describe the entire application you want in a single, high-level prompt in tools like Google AI Studio or Firebase Studio.
Generation: The AI generates the initial version of the full application, including the UI, backend logic, and file structure.
Iterative refinement: You test the application and use follow-up prompts to add new features or change existing ones.
Testing and validation: A human expert reviews the application for security, quality, and correctness.
Deployment: With a final prompt or a single click, you deploy the application to a scalable platform like Cloud Run.

Vibe coding versus traditional programming

With traditional programming, you focus on the details of implementation, manually writing the specific commands, keywords, and punctuation a language requires. Vibe coding lets you focus on the desired outcome instead, describing your goal in plain language, like "create a user login form," while the AI handles the actual code.

Here’s a comparison:

Feature	Traditional programming	Vibe coding
*Code Creation*	Manual coding line by line	AI-generated from natural language prompts
*Developer or user role*	Architect, implementer, debugger	Prompter, guide, tester, refiner
*Coding expertise required*	Higher (knowledge of programming languages and syntax)	Lower (understanding of the desired functionality)
*Primary input*	Precise code	Natural language prompts and feedback
*Development speed*	Generally slower, methodical	Potentially faster, particularly for prototyping simpler tasks
*Error handling*	Manual debugging based on code comprehension	Refinement through conversational feedback
*Learning curve*	Often steep	Potentially lower barrier to entry
*Code maintainability*	Relies on code quality, developer skill, and established practices	Can depend heavily on AI output quality and user review

Feature

Traditional programming

Vibe coding

Code Creation

Manual coding line by line

AI-generated from natural language prompts

Developer or user role

Architect, implementer, debugger

Prompter, guide, tester, refiner

Coding expertise required

Higher (knowledge of programming languages and syntax)

Lower (understanding of the desired functionality)

Primary input

Precise code

Natural language prompts and feedback

Development speed

Generally slower, methodical

Potentially faster, particularly for prototyping simpler tasks

Error handling

Manual debugging based on code comprehension

Refinement through conversational feedback

Learning curve

Often steep

Potentially lower barrier to entry

Code maintainability

Relies on code quality, developer skill, and established practices

Can depend heavily on AI output quality and user review

Getting started: Choosing your vibe coding tool

Google Cloud offers several tools for vibe coding. Choosing which tool you use should depend on your goal, and not necessarily your job title. A developer might use AI Studio for a quick prototype, an enthusiast might build a full application in Firebase Studio, and a data scientist might use Gemini Code Assist to write a script.

Once you are done prototyping, you deploy to Cloud Run, (for AI Studio and Firebase Studio), and can iterate from there using source code editing or go back to your vibe coding tool.

Use this guide to find the best tool for the task at hand.

Tool	Starting point	Skill level	Coding approach	Key feature
Google AI Studio	An idea you want to see, fast.	Beginner. No coding experience needed.	No-Code / Low-Code	Single-prompt app generation and one-click deployment. The fastest path from concept to a live, sharable application.
Firebase Studio	A new, full-stack application.	Beginner to intermediate. You can start with no code, but experience helps with customization.	Low-Code / No-Code	Full-stack generation with an integrated Firebase backend. Easily add a database, user authentication, and more.
Gemini Code Assist	An existing project or file.	Intermediate to advanced. Designed for users with professional coding experience.	Low-Code / AI-assisted	In-editor assistance. It generates, explains, and tests code directly within your existing IDE workflow

Tool

Starting point

Skill level

Coding approach

Key feature

Google AI Studio

An idea you want to see, fast.

Beginner. No coding experience needed.

No-Code / Low-Code

Single-prompt app generation and one-click deployment. The fastest path from concept to a live, sharable application.

Firebase Studio

A new, full-stack application.

Beginner to intermediate. You can start with no code, but experience helps with customization.

Low-Code / No-Code

Full-stack generation with an integrated Firebase backend. Easily add a database, user authentication, and more.

Gemini Code Assist

An existing project or file.

Intermediate to advanced. Designed for users with professional coding experience.

Low-Code / AI-assisted

In-editor assistance. It generates, explains, and tests code directly within your existing IDE workflow

How to vibe code with Google AI Studio

AI Studio is the quickest way to go from an idea to a live, shareable web app, often with a single prompt. It's perfect for rapid prototyping and building simple, generative AI applications.

Step 1: Describe what you want to build in your prompt

To get started, go to Build in AI Studio. In the main prompt area, simply describe the application you want to create. Start with a fun, creative idea, and then simply run the prompt. Once you run the prompt, you’ll see AI Studio generate the necessary code and files, with a live preview of your app appearing on the right-hand side.

Example prompt: "Create a 'startup name generator' app. It needs a text box where I can enter an industry, and a button. When I click the button, it shows a list of 10 creative names."

Step 2: Refine the app

Now that you have a live preview, you can use the chat interface to refine its look and functionality with follow-up prompts. You could add features, change visual elements, and more.

Example prompt: "Make the background a dark gray and use a bright green for the title and button to give it a 'techy' feel."

Step 3: Deploy to Cloud Run to share

Once you’re happy with the result, you can deploy your app directly to the web. Simply click the "Deploy to Cloud Run" button on the right-hand side menu, above your app preview. AI Studio will publish your app to a public URL, making it ready to share with your team or friends.

How to vibe code with Firebase Studio

Firebase Studio is a powerful, web-based environment for building production-ready applications, especially those that need a robust backend with features like user authentication or a database.

Step 1: Describe your full application or vision in your prompt

To get started, open Firebase Studio and describe the complete application you want to build in the prompt area. You can describe a robust, multi-page application from the very beginning.

Example prompt: Create a simple recipe-sharing application. It needs user accounts so people can sign up and log in. Once logged in, a user should be able to submit a new recipe with a title, ingredients, and instructions. All the submitted recipes should be displayed on the homepage.

Example prompt: Create a simple recipe-sharing application. It needs user accounts so people can sign up and log in. Once logged in, a user should be able to submit a new recipe with a title, ingredients, and instructions. All the submitted recipes should be displayed on the homepage.

Step 2: Review and refine the app blueprint

After submitting your initial prompt, Firebase Studio generates an app blueprint for you to review. This blueprint is a detailed plan outlining the features, style guidelines, and technology stack the AI intends to use.

Here, you can provide feedback to refine the blueprint, ensuring the initial code generation is closer to what you have in mind. Making changes to the plan at this stage is much easier than editing the final code, helping you get to your desired state faster.

Example prompt: This blueprint looks great, but let's remove the 'AI Meal Planner' feature for now and add a 'Favorites' button to the recipe display.

Example prompt: This blueprint looks great, but let's remove the 'AI Meal Planner' feature for now and add a 'Favorites' button to the recipe display.

Step 3: Generate the prototype

When you're happy with the blueprint, go ahead and click the "Prototype this App" button. Firebase Studio will then generate a working prototype based on your approved plan. After a moment, a live, interactive preview of your new app will appear.

Step 4: Make edits to your live prototype

With your interactive prototype running in the preview panel, you can continue the conversation to make edits. For example, ask for visual changes, add or change features, or even introduce new logic to your application.

Example prompt: Let's make that heart icon functional. When a signed-in user clicks on it, save the recipe to a 'favorites' list in their user profile in the database. Also, create a new 'My Favorites' page that only displays the recipes that the current user has saved.

Example prompt: Let's make that heart icon functional. When a signed-in user clicks on it, save the recipe to a 'favorites' list in their user profile in the database. Also, create a new 'My Favorites' page that only displays the recipes that the current user has saved.

Step 5: Deploy your application

When your application is ready, you can deploy it directly from the environment. To do so, simply click "Publish" in the top right-hand corner. Firebase Studio handles the entire deployment process, publishing your app to a public URL using Cloud Run. Because it's built for production, your application is ready to scale and handle traffic from day one.

How to vibe code with Gemini Code Assist

Gemini Code Assist acts as an AI pair programmer directly within your existing code editor (like VS Code or JetBrains). It’s best used for helping professional developers work faster and more efficiently directly in their IDE, and on existing projects.

Step 1: Generate code within a file

To get started, open a project file in your IDE. Instead of writing code manually, you can use the Gemini chat window or an in-line prompt to describe the function or code block you need. The AI will generate the code and insert it directly into your file.

Example prompt: "Write a Python function that takes a filename as input. It should use the pandas library to read a CSV file and return a list of all the values from the 'email' column."

Example prompt: "Write a Python function that takes a filename as input. It should use the pandas library to read a CSV file and return a list of all the values from the 'email' column."

Step 2: Refine and improve existing code

Highlight the code you just created (or any block of existing code) and use follow-up prompts to modify or improve it. This is perfect for adding new features, adding error handling, improving performance, or changing logic without having to manually refactor.

Example prompts: "That function is useful. Now, modify it to accept an optional 'domain_filter' parameter. If a domain is provided, the function should only return email addresses that match that specific domain."
"That's a good start, but it will crash if the user doesn't have permissions to read that file. Can you add error handling for a PermissionError?"

Example prompts: "That function is useful. Now, modify it to accept an optional 'domain_filter' parameter. If a domain is provided, the function should only return email addresses that match that specific domain."
"That's a good start, but it will crash if the user doesn't have permissions to read that file. Can you add error handling for a PermissionError?"

Step 3: Generate tests to complete the feature

To ensure your code is production-quality, you can ask Gemini to generate unit tests. This automates a crucial but often time-consuming part of app development.

Example prompt: "Write unit tests for this function using pytest. I need one test for the successful case that returns all emails, another test that filters for a specific domain, and a third test to handle a FileNotFoundError."

Example prompt: "Write unit tests for this function using pytest. I need one test for the successful case that returns all emails, another test that filters for a specific domain, and a third test to handle a FileNotFoundError."

Build from idea to application, faster

Vibe coding is more than just a new technique. It’s helping shift how we create software. It lowers the barrier to entry for new creators and acts as a powerful force multiplier for experienced developers, allowing everyone to focus more on creative problem-solving and less on manual implementation.