What is Vibe Coding?

Vibe coding is a trending term, primarily used on social media, that refers to a modern approach to software creation using AI. Instead of manually writing every line, users describe what they need, for example “build me a web based notepad app with Shadcn UI”, and the AI translates these instructions into working code. This method streamlines development by enabling rapid prototyping, automating repetitive tasks, and making programming more accessible to anyone, regardless of technical expertise.

Vibe coding can be a powerful tool for accelerating development, but to maximize its benefits, a structured approach is essential. In this article, we outline the pros and cons of vibe coding, along with the best practices based on our experiments.

Pros of Vibe Coding

Rapid Prototyping
Vibe coding allows people to quickly generate working code, making it ideal for testing ideas and building MVPs without starting from scratch.

Focus on High-Level Concepts
Instead of getting bogged down by syntax and structure, developers can describe functionality in natural language and let AI handle implementation, leading to more conceptual thinking.

Enhances Coding Discipline
Breaking down projects into incremental steps and refining AI-generated code fosters better structuring and problem-solving skills.

Engaging and Fun
Many users find vibe coding an enjoyable and almost gamified experience, keeping motivation high, especially during early project stages.

Accessibility for Non-Technical Users
Project managers, designers, and other non-developers can use vibe coding to better understand development processes, prototype ideas, and communicate with engineers.

Good for the Early Stages of a Project
Vibe coding is particularly effective for kickstarting projects, generating an initial structure, and rapidly iterating on ideas.

Potential for Large-Scale Implementation
While it works well for small projects, with proper guidance, it can also help scale complex systems into fully fledged apps.

Cons of Vibe Coding

Generated Code Often Needs Refinement
AI-generated code is rarely production-ready and often requires significant review, debugging, and optimization before deployment.

Lack of Structural Integrity Without Proper Guidance
AI has a tendency to produce messy, unstructured, or redundant code, leading to maintainability issues if not managed properly.

Encourages Over-Reliance on AI
Users who rely too heavily on vibe coding may lose touch with fundamental coding practices, making it harder to troubleshoot or manually refine solutions.

Technical Debt Accumulates Quickly
Making rapid AI-driven changes without careful management can lead to poorly designed codebases, increasing technical debt and long-term headaches.

Can Be Frustrating in the Long Run
While fun at first, dealing with AI-generated inconsistencies, errors, and unexpected behaviors can become tedious over time.

High Risk of Breaking Existing Code
AI doesn't always understand the full context of a project, leading to changes that can introduce regressions or make debugging difficult.

Tendency to Overload Files
Without explicit instruction, AI may lump too many functionalities into single files, making the project harder to maintain and scale.

Requires Strong Knowledge for Best Results
While AI assists with coding, achieving high-quality output still depends on the user's ability to guide prompts, recognize errors, and enforce architectural best practices.

Version Control Becomes Essential
Due to the unpredictability of AI-generated code, using version control is critical to revert unwanted changes and track iterations effectively.

Best Practices of Vibe Coding

1 - Planning and Preparation

Laying the Groundwork for Vibe Coding
Before starting any AI-assisted coding project, it is important to build a strong foundation. A methodical approach helps ensure that your project runs smoothly from the outset.

Clarifying Your Project Objectives
Begin by clearly defining what your project is meant to achieve. A detailed outline of your application's purpose sets the stage for every subsequent decision in the development process.

Crafting a Detailed Project Plan
Invest time in planning your project. Consider all requirements and functionalities. Think about not only the final product but also how you will get there. This preparation leads to a more structured and efficient coding phase.

Segmenting Your Project into Manageable Parts
Break down your project into smaller, manageable components. This approach allows you to focus on individual parts without feeling overwhelmed by the entire codebase. Each component can be developed and tested independently.

Organizing Your File Structure
Plan a logical file structure for your project early on. An organized file system simplifies navigation through your code and helps maintain scalability. A clean structure also aids in collaboration with others and future maintenance.

Understanding the Technologies Involved
Gain a thorough understanding of the platforms and technologies you are using. Whether you are working with tools like Supabase, frameworks like React or diverse libraries like Three.js, having insight into their inner workings is crucial for effectively guiding AI outputs.

Integrating External Tools and Frameworks
If your project involves integrating external tools, it is important to know how these tools function. This knowledge will empower you to adapt AI-generated suggestions to better fit the tools’ specific requirements.

Distinguishing Between Reusable and Unique Code
Recognize that AI might produce redundant code. It is essential to identify segments that can be modularized for reuse versus those that are unique. While reducing duplication is often beneficial, there are times when small duplicates are acceptable to maintain clarity and ease of debugging.

Taking a Measured Approach to Development
Avoid rushing through the setup phase. Devote sufficient time to establishing a solid architecture. By doing so, you lay the groundwork for a smoother and more efficient coding process, reducing the likelihood of complications later on.

2 - Interacting with AI

Detailed and Specific Instructions
Precision is essential. For example, instead of broadly requesting AI to create a "web based notepad app", you might clearly instruct:

Using React, build a web-based notepad application with Shadcn UI components and Supabase for backend storage. Users should be able to sign up, log in, create, edit, delete, and securely save notes. Implement real-time syncing for note updates. Follow the project structure outlined in the attached documentation, clearly specifying filenames and file paths. Refer directly to the documentation wherever possible to ensure consistency.

Explicitly mentioning file names and paths helps minimize confusion. Additionally, when describing UI or visual tasks, providing screenshots offers the AI valuable context about your expectations.

Iterative Prompting and Process Orientation
Effective prompting isn't solely about the final output. Recognize that AI learns and adapts based on conversational context. If the initial AI response doesn’t meet expectations, consider refining or adjusting your prompt instead of discarding the effort. Iterative adjustments typically yield more accurate and targeted outcomes.

Managing Context and Conversation Hygiene
AI retains conversational context throughout a session, which can occasionally cause confusion or errors (context pollution). If the AI repeatedly misunderstands or generates incorrect assumptions, it’s beneficial to start fresh. Initiating a new chat session or forking a project can clear accumulated confusion and restore clarity, depending on what tool you're using.

Capitalizing on the "Just Talk" Approach
We get the best results when we explicitly just talk with AI about the solution before starting implementation. Instead of immediately asking for a finalized solution, first discuss diverse methods and ask AI for various suggestions. Although AI doesn’t always choose the optimal decision on its own, it often provides valuable insights and alternative approaches when prompted. By carefully evaluating these suggestions, you can then confidently determine the best course of action.

We've noticed that this conversational method ultimately leads even the AI itself to make better decisions when it is first asked for diverse methods and suggestions.

3 - Code Management

Structured Code Management
Without structured management, vibe coding can quickly become chaotic. Maintaining organization ensures code remains readable, maintainable, and efficient, especially when collaborating with AI.

Keep Files Small and Manageable
Avoid allowing individual files to exceed around 700-1000 lines, as AI tools have limited context windows and struggle with larger files. Instead, break your code into smaller, focused components. Our observations suggest optimal AI performance varies by tool, Bolt handles code effectively up to approximately 600 lines recently, whereas Cursor performs reliably up to about 1000 lines. Performance typically declines upon hitting these thresholds.

Regularly Monitor for Duplication and Obsolete Code
Over successive iterations, AI-generated code often accumulates duplicates or unused sections. To mitigate this, periodically request that the AI carefully optimize and clean your files. Ensure this optimization doesn't inadvertently remove critical functionality or code segments that are still needed.

Implement Strategic Debugging Techniques
Strategically placing console logs can significantly enhance debugging efficiency. However, always remove these logs after resolving the relevant issues to maintain code cleanliness. Additionally, AI-generated solutions can overlook edge cases, highlighting the importance of comprehensive testing and active debugging to ensure robustness.

Leverage Version Control for Safety and Flexibility
Effective version control ensures you always have reversible changes available. Maintain proper backups, commit frequently, and use branches when integrating significant AI-generated code. This practice prevents disruptions in your primary codebase and allows safer exploration of AI-driven changes.

4 - Technical Knowledge & AI Guidance

Maintaining Fundamental Understanding
Even though AI can significantly streamline the coding process, developers must maintain a solid grasp of fundamental programming concepts. Deep technical understanding ensures better results from AI-generated code.

Clearly Grasp Your Application’s Technical Architecture
AI-generated solutions improve markedly when you understand your application's core technical elements. This includes differentiating frontend and backend responsibilities, managing state effectively, handling API interactions clearly, understanding database structures and efficient querying methods, and practicing strong authentication and security measures.

Prepare to Debug and Refine AI-Generated Outputs
AI is best utilized as an assistant rather than a substitute for careful, knowledgeable development. Users should anticipate that AI might produce errors, inefficient solutions, or security vulnerabilities. Therefore, always critically review, debug, and refine AI-generated code before deploying it to production environments.

5 - Using the Right Tools

Selecting the right tools can enhance the efficiency and effectiveness of vibe coding. Tools like Bolt, Lovable, Cursor, Windsurf, Replit, or v0 are optimized for AI-assisted coding and help streamline workflow.

Experiment with Multiple Tools
Different tools work better for different tasks. If one isn’t producing optimal results, try another. We found it best to always use multiple in the same time.

See our article about the best AI App Builders:

Best No-Code & AI App Builders: From Bolt New to Cursor and Beyond

Learn about the best no-code and AI app builders like Bolt.new, Lovable, v0, Cursor, and Windsurf to revolutionise product development.

HirakuStef Buzas

6 - Security & Best Practices

Prioritizing Security
Security should never be an afterthought, particularly when incorporating AI-generated code into your projects. Proactively embedding security considerations into the development process protects your users and your business.

Explicitly Request Security Measures in Your Prompts
When interacting with AI, explicitly ask for security best practices within your prompts. For example, rather than simply requesting a login system, you might instruct:

Implement secure authentication that includes hashed passwords, multi-factor authentication, and encryption of user data before storing it in the database.

Being specific ensures the AI generates solutions that align with best security practices.

Leverage Trusted Third-Party Services
For critical functionalities like payments, authentication, or handling sensitive data, rely on established third-party services such as Stripe, Firebase, or Auth0. Using well-tested, industry-standard solutions significantly reduces risk compared to depending solely on AI-generated code.

Regularly Conduct Security Audits
Projects involving personal or sensitive user data should regularly undergo thorough manual security reviews. For high-stakes or complex projects, professional security audits may also be necessary to ensure vulnerabilities are identified and mitigated effectively.

7 - Iteration & Optimization

Effective Iterative Development
Successful integration of AI-generated code involves structured, iterative development practices. Refining and evolving the solution step-by-step is crucial to achieving reliable, high-quality outcomes.

Focused, Feature-by-Feature Development
Instead of generating extensive sections of code simultaneously, approach development incrementally, feature by feature. Begin by clearly defining individual components or small features, allowing for better manageability and more accurate results. Working incrementally helps you maintain control over code quality and clearly isolate any issues.

Continuous Testing and Iterative Improvements
Regular testing is essential to detect and resolve errors early. Expect multiple rounds of revisions and improvements, guided by ongoing feedback from tests and practical evaluation. AI-generated code often requires several cycles of iteration before becoming stable and production-ready.

Refine AI Outputs Through Regular Iteration
Be prepared for AI-generated code to evolve through repeated refinement cycles. If initial outputs don't fully meet expectations, iterative prompting, combined with testing and user feedback, progressively leads to polished, robust solutions. Keep in mind previous versions that you can always revert to.

Final Thoughts

Vibe coding is a powerful tool that speeds up development and makes programming more accessible. It works best when used strategically, leveraging AI for initial drafts while applying strong engineering practices to refine and structure the final output. Users who balance AI assistance with manual oversight will get the most out of vibe coding while avoiding its pitfalls.