What is vibe coding?

In software engineering, development is reshaping from strict, manual coding and becoming more flexible, AI-assisted and AI-powered through generative AI and AI-assisted coding. Vibe coding is at the forefront of this change. “Vibe coding” represents a shift toward intent-driven software development, where AI systems assist developers by generating code from natural language instructions.

“Vibe coding” was introduced by renowned Computer scientist Andrej Karpathy in February 2025. According to Andrej Karpathy, it’s a coding strategy that involves minimal human coding because programmers and coders use AI codes in their programming tasks but concentrate more on how the result comes out, testing and fine-tuning. He also emphasized the significance of AI tools, coding tools and AI coding assistants in software development. 

The field of vibe coding has moved from basic prompts for code to a deeper understanding of how coding works when focused around context, intention and orchestration. The further implementation of AI moves into actual tasks, the distinction will no longer be between specific programming languages and syntax but specifying intentions and contexts.

• Quick prototyping: As vibe coding continues to gain traction in the ecosystem, rapid prototyping is becoming a key enabler for teams to move ideas from early-stage concepts to functional prototype to scale. This ability is valuable for testing ideas in the market to see whether that idea will actually solve a problem or if strategy needs to change altogether.
• Problem first approach: Moving from building code with a rigid coding style to dynamic structure enables swift innovation as the act of solving the problem becomes the cornerstone for developers over the tech stack itself.
• Reduce risk, maximize impact: Vibe coding will enable businesses to quickly progress with a minimum viable product (MVP), cheaply experiment with ideas and adopt and adapt based on the feedback. This speed lowers sunk costs, increases the spread of risk and allows for pivoting because resources are only committed to validated concepts.
• Multimodal switch: Vibe coding is evolving into multimodal programming with voice, visual and text-based coding to enhance productivity. Some of the most fascinating trends include voice-driven coding, visual programming interfaces, AI-enabled code generation and hybrid development environments. These trends improve coding’s usability, flexibility and intuitiveness, reshaping the landscape of software development.

To implement vibe coding in practice, the user needs to follow the following steps:

Step 1: Selecting the AI coding platform

Start by choosing an AI coding platform and vibe coding tools that suit your needs. Current AI coding platforms like Replit, Cursor, GitHub Copilot, Windsurf and bolt.new provide more than just autocomplete features. They can perform real-time code generation, multifile reasoning, debugging, command execution, deployment help and workspace memory within the IDE, development environment or code editor.

Step 2: Describe the intent and context

Vibe Coding does not rely heavily on the specific prompts provided but instead focuses on the context given. Before developing code, it is important to state the objective and requirements of the application along with the constraints, preferred technology stack and design style. 

Let’s take an example to understand.

Create a web app for a new startup that will be used for habit tracking. Users should be able to create habits and track their completion rate each day. Create graphs that illustrate how the user performs and provide a minimalist dark-mode interface. Use React for front-end and Node.js or Python for back-end development. Incorporating the requirements along with the right context makes it easier for AI helpers from providers like Anthropic to produce output that is more correct and adheres to the right architecture. The prompt is clearly specific, context relevant and goal oriented.

Step 3: Generate the initial application code

With the input of the intent and context provided, the AI produces the first draft of the application. The output could range from application architecture, front end code, back end code, API, database design and relevant documentation. What is produced is merely the starting point for building the application.

Step 4: Iteratively refine the generated software

The development process of AI code production is iterative, requiring constant iteration. After reviewing what was created initially, the developers will work on refining the application and adding new features. They can adjust the requirements, change functionality, enhance performance, improve usability, perform code refactoring, redesign the application architecture or anything else. This means that developers follow this cycle: Intent → Generate → Review → Refine → Generate

Step 5: Validation, security and deployment

Like traditionally written software code, the generated software still needs engineering efforts to be put in production. It requires code review, testing, dependency checking, security assessment for potential vulnerabilities, compliance verification and more to ensure high code quality. It means that developers aim to create secure, reliable, scalable, maintainable and production-quality software.

The reason for the failure of any AI model is never a bad prompt. AI models fail because of the absence of any context. Future advancements in large language models, or LLMs, will come from the area of context rather than prompts. For an AI model like Gemini to produce a reliable output, it must be given contexts such as:

• Business context—What problem are we trying to solve?
• Architectural context—How does our system integrate into the existing architecture?
• Repository context—What conventions, patterns and dependencies already exist in the codebase
• Security context—What policies, compliances and constraints do we have to follow?
• Operational context—What performance and scalability concerns do we have to meet?

If an AI model fails to take any of these issues into account while generating output, then it might create code that is right technically, but wrong in a real-world operational ecosystem. Therefore, context engineering is going to become a core competency in enterprise AI to generate context-aware output.

Vibe coding was a breakthrough that proved AI could generate code from natural language prompts, making software prototyping faster and more efficient. However, the generation of code is not all the way to software engineering. The production software development requires planning, architecture design, code generation, testing, security reviews, deployment and governance. This is where vibe coding starts showing its flaws.

What we are now seeing the industry move toward is the shift from prompt to code toward intent to AI agent to software. With this approach, the emphasis is not on prompt-based code generation but rather on the entire software engineering process. That way modern software development does not face the challenge of code generation anymore as it covers automation and orchestrating different software engineering tasks at every stage of development.

Vibe coding is undoubtedly potent. However it has some technical and real-world challenges. Here are some of the key challenges listed:

1. Technical complexity: Vibe coding can handle basic standard frameworks but for real-world applications where technical requirements can be novel or complex, vibe coding becomes challenging.³
2. Code quality and performance issues: Vibe coding is helpful to test applications and create prototypes, but it still requires optimization and refinement to make sure that the code quality is maintained. Vibe coding is not an ideal choice for distributed applications because it requires structured level architecture and sophisticated optimization strategies.
3. Debugging challenges: Code generated by AI is challenging to debug because it’s dynamic and lacks architectural structure.
4. Maintenance and updates: Many times, if the software application is not updated in a timely manner, it becomes outdated, which is why they require regular maintenance as well as updates. Applications built by using AI-generated code face maintenance and update challenges if the code structure is not properly maintained. This issue can cause developers to struggle to understand the underlying logic when trying to keep it updated and optimized.
5. Security concerns: This limitation is the most critical as it can lead to several risks and extreme vulnerabilities. Code generated by using AI is often excluded from code reviews and security checks, leading to unseen vulnerabilities that can go unnoticed and be exploited.

Vibe coding caused a new type of technical debt called security debt. The more developers embrace AI coding without validation, the more vulnerabilities that are likely to arise and be fixed at some point in the future.

Examples of these vulnerabilities include: API keys and passwords hardcoded into the application, vulnerabilities to SQL injections, unsecured APIs, overprivileged applications, inadequate authentication and third-party libraries with vulnerabilities.

Regarding enterprise-level environments, the stakes are higher because the initial vulnerable prototype created by using vibe coding might later turn into an actual production-ready application. That application manages customers’ information, financial details, medical information, intellectual property rights and other company processes. What is initially a quick prototype might turn into a crucial business application without going through the necessary security analysis. The issues shift from “we can create this” to “can we trust this?”

Software engineering is experiencing changes in its economic models due to vibe coding. Traditionally, the production of software has been limited by the coding expertise and efforts required, particularly among beginners.

With the emergence of artificial intelligence, the bottleneck shifts from coding itself to ideas and decisions because coding will be available in abundance. There has long been a dispute over whether AI can be used to write software. However, the more relevant question that arises in today’s world is whether AI can be properly leveraged to accelerate innovation and bring products into the market.