Top 5 Challenges in Vibe Coding and How to Overcome Them

Imagine you’re building a software application, and instead of writing every line of code yourself, you simply describe what you need in natural language, and bam—AI generates the code for you. Sounds like something straight out of a futuristic tech movie, right? Well, this is the magic of vibe coding, a rapidly emerging trend changing how developers work in 2025. It promises lightning-fast development, reducing the complexity of coding and making it more accessible for everyone.

But, as with any game-changing technology, vibe coding isn’t without its risks. While it can supercharge your workflow, it can also introduce new challenges that, if left unchecked, could hurt your applications' quality, security, and scalability. Let’s check out the 5 major risks of vibe coding and explore simple, actionable ways to avoid them, ensuring your codebase stays healthy, secure, and future-proof.
Ready to learn how to navigate the vibe coding revolution? Let’s jump in!

Why Vibe Coding is a Game-Changer

Vibe coding is more than just speed; it fundamentally alters the software development. AI now generates 41% of all new code, with 256 billion lines written in 2024 alone, indicating its widespread adoption. Here's why:

• Makes software development accessible - Non-developers may now create viable apps without requiring years of coding skill.
• Significantly accelerates development - reduces time spent on repetitive coding jobs and debugging.
• Perfect for prototyping - Ideal for startups and product teams that need to move quickly.
• Increases developer productivity - Engineers may focus on issue solutions rather than manual coding.
• Of course, others claim that AI-generated code may not always be optimised.

The Future of Vibe Coding

AI-powered development is only beginning. Here's where it is going:

• Greta is now developing full-scale apps based on short descriptions!
• AI that understands business logic - Future AI models will incorporate domain-specific knowledge, making industry-specific apps easier to develop.
• More collaborative AI coding - Teams will naturally work alongside AI, with AI tackling monotonous chores and humans focussing on innovation.

Top 5 Challenges in Vibe Coding and to Overcome Them

1. Technical Debt Accumulation

The Risk: Speeding up development by relying on vibe coding often leads to technical debt. AI-generated code tends to be inconsistent, poorly documented, and structured in a way that’s difficult to maintain long term. Kevin Scott predicts that within the next five years, 95% of code will be generated by AI, with minimal line-by-line coding done by humans.

How to Avoid It:

• Conduct Regular Code Reviews: Establish a robust review process specifically for AI-generated code, ensuring consistency and clarity.
• Track Technical Debt: Use tools like SonarQube to monitor technical debt and prioritize refactoring efforts.
• Schedule Refactoring Sprints: Allocate specific sprints for cleaning up and improving code quality, preventing debt from accumulating.

2. Security Vulnerabilities

The Risk: AI often overlooks important security aspects, such as input validation, error handling, and dependency management, leading to potential vulnerabilities in the application.

How to Avoid It:

• Automated Security Scanning: Use tools like OWASP ZAP and Snyk to automatically scan AI-generated code for common vulnerabilities.
• Security-Focused Code Reviews: Conduct regular reviews with a security focus to spot issues like improper error handling or weak validation.
• Security Training: Ensure developers are trained to spot and mitigate common vulnerabilities in AI-generated code.

3. Scalability Issues

The Risk: While vibe coding is great for rapid prototyping, it often leads to applications that struggle to scale effectively. AI-generated solutions may focus on short-term functionality rather than long-term performance, leading to inefficiencies as the app grows.

How to Avoid It:

• Set Performance Benchmarks: Regularly test AI-generated code against performance benchmarks to identify scalability issues early.
• Adopt Microservices Architecture: Break down applications into smaller, independently scalable components, ensuring that scalability is built into the design.
• Review Resource Utilization: Pay special attention to database optimization, caching strategies, and efficient resource use to avoid bottlenecks.

4. Reduced Code Maintainability

The Risk: AI-generated code often lacks clarity, structure, and consistency, making it difficult to maintain in the long run. This can lead to frustration among developers, especially when they need to debug or enhance the code.

How to Avoid It:

• Enforce Coding Standards: Establish clear standards for AI-generated code and ensure they are followed throughout the development process.
• Require Documentation: Demand comprehensive documentation for all AI-generated code, explaining not just what the code does but why certain decisions were made.
• Pair Programming: Implement pair programming where one developer focuses on understanding and documenting the code while the other refactors it for clarity.

5. Testing Gaps

The Risk: Insufficient testing is a common issue with vibe-coded applications, as developers might not fully understand the AI-generated code. This results in incomplete test coverage and inconsistent testing practices, ultimately leading to poor-quality releases.

How to Avoid It:

• Implement Test-Driven Development (TDD): Write tests before generating AI code to ensure it meets the necessary requirements.
• Establish Continuous Integration (CI): Set up automated CI pipelines that run comprehensive test suites on all changes, including AI-generated code.
• Set Minimum Test Coverage: Define and enforce test coverage requirements before code is merged into the main codebase.

How Greta Leads the Way for Vibe Coding?

Greta is designed to help developers overcome the challenges of vibe coding by offering a comprehensive suite of tools and features that streamline the process. With its advanced code analysis capabilities, Greta automatically identifies potential issues in AI-generated code, such as inconsistencies, security vulnerabilities, and scalability concerns.

By leveraging Greta’s real-time code review system, developers can pinpoint technical debt early and make necessary adjustments before it accumulates. Additionally, Greta supports security scanning to detect and address common vulnerabilities, ensuring that AI-generated code meets industry standards.

Greta also fosters maintainability by providing clear documentation generation and code structure visualization, making it easier for teams to understand and modify AI-generated code in the long run. With its automated testing integration, Greta ensures comprehensive test coverage, reducing the risks of missed bugs and improving code quality.

In short, Greta equips developers with the tools they need to balance the speed of vibe coding with high-quality, secure, and scalable code.

Conclusion

Vibe coding presents an exciting frontier in software development, offering the potential to streamline the coding process and make development more accessible. However, it's essential to approach this paradigm with a balanced perspective, acknowledging its challenges and implementing strategies to overcome them. By doing so, developers can harness the power of AI while maintaining control over the quality, security, and integrity of their code.

If you're interested in exploring vibe coding further and need a platform that supports this approach, consider checking out Greta. Greta offers tools and resources to help you integrate AI into your development workflow effectively.

Remember, while AI can be a powerful ally in coding, the best results come from a collaborative approach where human expertise and AI capabilities work hand in hand.

FAQs

1. What is vibe coding?

Vibe coding is an AI-assisted development approach where developers describe the desired functionality in natural language, and AI generates the corresponding code. This process speeds up development and makes coding more accessible.

2. What are the common challenges in vibe coding?

The main challenges include technical debt, security vulnerabilities, scalability issues, reduced maintainability, and testing gaps in the generated code.

3. How can technical debt be avoided in vibe coding?

Implementing regular code reviews, tracking technical debt with tools like Greta, and dedicating sprint cycles to refactoring AI-generated code can help mitigate technical debt.

4. How do security vulnerabilities arise in vibe coding?

AI models may overlook critical security aspects such as input validation and error handling. Regular security reviews and automated scanning tools can help detect and prevent vulnerabilities.

5. Why is testing important in vibe coding?

Insufficient testing can lead to bugs and regressions in AI-generated code. Adopting test-driven development (TDD) and integrating continuous testing practices can ensure high-quality releases.