What is Vibe Coding?

ChatGPT was asked the meaning of vibe coding and replied with literal “vibing” – listening to music and having theme-based surroundings while you code away! A large section of people would think in the same way, so you can’t exactly blame the poor AI. But what is this ‘vibe coding’ that’s been trending among the coding community and enthusiasts? Read on to find out what it is all about.

Check out this video by Entri in Malayalam!

Introduction: Vibe Coding

The world of coding and development has seen a lot of changes over the years, with AI being the latest entry. Gone are the days when one had to spend hours and days developing running codes and programmes. The influence of AI has been so massive that a whole new coding method was born – vibe coding. In February 2025, Andrej Karpathy, a renowned computer scientist, introduced and established this method.

Vibe coding isn’t just about using AI as another tool – it is more about using AI features to enter a creative flow state, where a collaboration between developers and AI takes place to bring life to ideas in a quicker manner. During a time when there are daily debates on whether AI will take over the world, vibe coding redefines the usage of AI to reduce mental overhead, accelerate prototyping, and shift the developer’s role from implementer to orchestrator.

Being a fresh new perspective, vibe coding lets users express their intention through simple language to get an executable code. The goal of the term is to have AI work as assistants for developers and do tedious work such as making suggestions in real time, automating processes, and even generating standard codebase structures. [1]

What’s in the Core of Vibe Coding?

The core idea of vibe coding is simple: code first, optimise later. This is a major leap in terms of letting developers prioritise building the code first and optimising it later. Since vibe coding aligns with the principles of iterative development, cyclical feedback loops, and fast prototyping, enterprises can foster innovation, instinctive problem-solving, and flexible coding capabilities together.

However, as with all cases, AI only generates the code; the human element, consisting of true creativity, goal alignment, and out-of-the-box thinking, makes the difference, making human oversight a necessity.

Key Pillars of Vibe Coding

• Say goodbye to constantly switching contexts and tabbing through Stack Overflow, as vibe coding allows you to stay in the flow.
• Instead of being a reactive, fixing-your-mistake partner, AI becomes proactive, suggesting, debugging, and even architecting code for you.
• The AI works best when you provide all the necessary context, such as file structure, variable names, and even the previous prompts to help generate smarter outputs.
• The process involves starting with an imperfect code and then refining it collaboratively with AI, emphasising iteration over perfection.

 The Vibe Coding Stack

We have broken down the key tools that made vibe coding possible into 4 categories for ease of understanding:

• Code Generation

  • GitHub Copilot: Copilot helps auto-complete, suggest functions, and even generate full modules due to its extensive training in vast amounts of public code. Additionally, it is integrated into IDEs like VS Code and JetBrains.
  • ChatGPT: Excelling in logic generation, code walkthroughs, and high-level planning, ChatGPT – the AI that took the market by storm – can explain, write, and review code in multiple languages.
  • Replit Ghostwriter: Replit’s AI, also known as the ideal companion for beginners and solo developers, is geared for quick prototyping and education.
  • Amazon CodeWhisperer: With enterprise-grade security practices and AWS service integration, CodeWhisperer is designed for enterprise and cloud developers.

• Debugging and Review

  • Codeium and Sourcery are AI tools that can help refactor code or highlight inefficiencies, especially in logic. Additionally, they can suggest cleaner versions of functions and even alert you to potential bugs.
  • AskTheCode is another GPT-backed tool, where you can ask questions about the code, such as something specific, like asking for the location of variables defined, or something broader, like why the API is failing altogether!

• Documentation and Learning

  • A lot many available tools can now generate documentation automatically. For example, GitHub Copilot can insert inline commands, while ChatGPT can provide code summaries and even generate complete README files.
  • AI can even wear the hat of a teacher and provide answers to your various doubts while working, without switching tabs.

• Workspace Integration

  • Cursor IDE offers ChatGPT directly into the editor, thus allowing full conversational context.
  • You can make use of terminal environments like Warp to integrate AI into the command line, explaining shell commands and helping with Git workflows.

Vibe Coding: Step-by-Step Guide to Implementation

The process may seem a bit overwhelming, but when you simplify the steps as given below, you get to vibe while coding, literally.

Step 1: Choose an AI coding Assistant

This step solely depends on your technical, performance, and cost requirements, as choosing the right AI for your requirements results in quicker and more seamless execution. Replit is an example of the many dynamic platforms for turning ideas into executable code.

Step 2: Defining the prompt/requirement

This step is a defining moment as this step would develop the foundation of your idea. Give a clear prompt describing your requirement and what you want the output to be. The more you specify the requirement, the more effective your output will be. Try to have a clear mind map of how your idea needs to be materialised and give the order. The following example has a specific, clear objective that is established from the very beginning.

Example: Hey! I have been meaning to create something for a while. I need you to build a full-featured blog backend with Node.js, Express, and SQLite. There should be a user authentication that allows users to register and log in, store passwords securely using bcrypt, and protect all post-related routes with auth middleware……

Step 3: Refining the Code

The prompt would give an output that is pretty basic and imperfect, which would be considered a starting point for refinement. The basic version of the code would define the scope of refinement through more prompts.

Step 4: The Final Step – Review

In this final step, the refined and optimised code is reviewed after various prompts, until it is ready for deployment.

Usage of Vibe Coding

Vibe Coding has found its place among the new developers, especially in the new startups within the Y Combinator platform.[2] The buzzword has turned into a full-fledged favorite of many coders and users with no prior skills to develop specific applications.

Communities have been formed on various platforms, like the one on Reddit for sharing experiments and useful rants about the phenomenon.[3] The use cases of vibe coding are dynamic and ever-evolving for any sort of prediction as of now. However, it is safe to say that multi-million-dollar worth of vibe-coded apps and software will be ruling the market soon.

Limitations of Vibe Coding

Though having AI by your side while coding feels almost magical, developers and coders need to stay grounded and understand the limitations of AI, or more specifically, where it shines and where it stumbles. Moreover, a critical sense needs to be developed for approaching outputs produced by AI.

• Over-reliance on AI

Working with AI gives rise to a habit of blindly copy-pasting the code given by the AI, without any review. This is especially common when AI gives quick fixes and complex solutions that do the job. Regardless of having AI do most of the work, without careful inspection, you might ship buggy, inefficient, or insecure code, which may even stint your growth as a developer.

To stay grounded, you can treat code from AI as a suggestion from a coworker, which should be reviewed. Review why a code works instead of how it works. Also, try switching off the AI and solving problems on your own to stay sharp on your skills.

• Quality over Quantity

Your prompt may be long, but unless it’s explained clearly, the output will be garbage. Being vague and overly complex can result in the AI misinterpreting your intent or producing a code that sounds right, but misses the sweet spot. You may end up debugging code that’s working on something different, leading to a loss of time and effort.

Change the way you provide the prompt as if you are explaining something to a junior developer – clear, step-by-step instructions, with bigger tasks broken down into smaller chunks. Make it a habit to ask follow-up questions to refine the output.

• Trusting the Code

By now, we have an understanding that AI can produce code, but its authenticity is what matters. It may look syntactically correct, but it can be subtly flawed, insecure, or even outdated. This might lead to you introducing security vulnerabilities like bad auth logic or SQL injection, performance issues, or even copyright issues if AI sources the code from public repos.

Always check your code thoroughly by running security linters or tools like ESLint, SonarQube, or npm audit. You can also cross-check implementations that are logic-heavy against official docs or community standards.

• Context Deficiency

AI may not always know what files you’re working on, or what dependencies you’ve installed, or how your codebase is structured unless you inform it of these aspects. Without such knowledge, the code produced by the AI may reference patterns and functions that are not present in your setup. Overcomplicating prompts and requirements can lead to AI not functioning properly and even embarrassing yourself![4]

To work your way around this, remember to feed the relevant context, including file structure, imports, or existing functions. You can even consider using tools like Copilot or Cursor that are IDE-integrated and can have deeper access to your codebase.

• Lack of Clarity in the Ownership of the Code

This is a question that pops up in the mind of vibe coders – If AI writes a major portion of the code, who exactly owns it? This can even be tricky in team settings or when using AI-generated code in production or commercial tools. This may lead to problems related to licensing, accountability, and code reviews.

AI codes are to be approached as a draft, making you the author who approves or refactors it. If you are in a team setting, make sure to document and comment clearly. Avoid blindly pasting full AI-generated content into production systems.

Final Thoughts…

Vibe coding has led to the evolution of VibeOps, an operational extension that has integrated AI into DevOps tasks. It is being driven by the growing complexity and limits of traditional software development, but as the capabilities emerge, it is anticipated to gain popularity. AI-driven automation reduces costs and allows the developers to focus only on innovation, along with a dynamic strategy wherein AI complements human knowledge instead of replacing it altogether.

Vibe coding is still in its infancy, with scope for a lot of improvement and reception. However, due to its feature of easing the lives of coders with automation, it equips programmers and nonprogrammers with real-time production and workflow efficiencies. However, the bottom line is that human intervention is a necessity for achieving the intended outcome for the user.

References

1. Andrej Karpathy on X
2. Y Combinator on LinkedIn – “For 25% of the Winter 2025 batch, 95% of lines of code are LLM generated. That’s not a typo. The age of vibe coding is here.”
3. Vibe Coding thread on Reddit
4. Ars Technica – “AI coding assistant refuses to write code, tells user to learn programming instead”