Vibe coding

What is vibe coding?

Vibe coding is an AI-driven software development approach where developers describe desired functionalities in natural language prompts. Large Language Models (LLMs) like ChatGPT or Claude then generate the corresponding code, shifting the developer’s role from manual coding to guiding and refining AI-generated outputs. Coined by Andrej Karpathy in 2025, vibe coding emphasizes intuition and creativity over traditional coding syntax and structure.

What is the origin and evolution of vibe coding?

The term “vibe coding” emerged from the AI community, highlighting a shift towards more intuitive coding practices. With advancements in LLMs, developers began leveraging AI to handle routine coding tasks, allowing them to focus on higher-level design and problem-solving. This evolution democratized software development, enabling individuals without formal coding backgrounds to build functional applications.

How does vibe coding work?

In vibe coding, developers interact with AI tools in a dynamic and collaborative workflow that leverages both natural language input and iterative code generation. The typical process includes:

• Describing features: Developers begin by articulating the application’s goals or specific features in plain language prompts. This step often resembles product ideation sessions, where user needs and business objectives are outlined.
• Prompt engineering: Fine-tuning the phrasing and specificity of prompts plays a crucial role in achieving high-quality results. Developers experiment with different instructions, formats, and levels of detail to guide the AI effectively.
• Code generation: Based on the prompts, the AI generates code—ranging from simple functions to complex UI components. Developers review this output for correctness, efficiency, and alignment with the intended functionality.
• Iterative refinement: This phase involves continuously updating prompts, tweaking code manually when necessary, and re-engaging the AI to improve the solution. It is common to cycle through several rounds of back-and-forth before reaching the desired outcome.
• Testing and validation: Developers perform unit tests, integration tests, and manual validation to ensure the generated code works as intended and doesn’t introduce regressions or security flaws.
• Deployment: Once validated, the feature is merged into the main codebase and deployed. Continuous monitoring tools like Pendo are often used post-deployment to analyze usage and performance.
  

This methodology fosters a powerful synergy between human intuition and AI efficiency, allowing developers to shift focus from syntax and boilerplate code to architecture, usability, and business impact.

What are the pros and cons of vibe coding?

Pros:

• Accelerated development: Rapid prototyping and iteration allow SaaS teams to move from idea to implementation much faster than traditional development. For example, a product manager can describe a feature—like a custom onboarding flow for enterprise users—and have a functioning prototype within hours, not days.
  
• Accessibility: Vibe coding lowers the barrier to entry for non-developers, enabling designers, product managers, and even customer success professionals to contribute to product development. Imagine a CS team member generating a basic reporting dashboard for user engagement metrics using AI, which a developer can then polish and integrate.
• Focus on creativity: By offloading routine and repetitive tasks to AI, developers can focus on what truly matters—innovative problem solving and UX. For instance, rather than manually coding every modal or button state, engineers can spend their time crafting a unique, seamless workflow for B2B trial conversions.

Cons:

• Potential for errors: AI-generated code may contain bugs, inefficient logic, or unintended behavior. For example, in a SaaS billing module, an AI might incorrectly apply tax logic across international regions, leading to compliance issues. Manual review and QA testing remain essential to ensure production-ready code.
  
• Security concerns: Without thorough review, AI-generated code can expose security vulnerabilities—such as open API endpoints or improper data validation. Imagine a scenario where a vibe-coded feature allows unrestricted file uploads without size limits or content checks—this can quickly become a vector for abuse.
• Over-reliance on AI: Depending too heavily on AI tools may diminish hands-on coding skills over time. For instance, a junior developer might struggle to debug or scale a user management feature built by AI if they never fully understood how it was constructed. To mitigate this, teams should ensure regular upskilling and code walkthroughs.

How does vibe coding affect SaaS development?

Vibe coding is particularly beneficial in SaaS development, where speed and adaptability are crucial. By leveraging AI, teams can quickly prototype features, test user interactions, and iterate based on feedback. This approach supports agile methodologies and continuous delivery models prevalent in SaaS environments.

What is the vibe coding process?

1. Ideation: Define the application’s purpose and key features.
2. Prompting: Communicate desired functionalities to the AI tool.
3. Generation: AI produces the initial codebase.
4. Review: Assess and refine the code for performance and security.
5. Testing: Validate the application’s functionality.
6. Deployment: Launch the application to users.

What are vibe coding best practices?

• Clear communication: Provide precise and detailed prompts to the AI.
• Continuous learning: Stay updated with AI tool capabilities and limitations.
• Thorough testing: Implement rigorous testing protocols to ensure reliability.
• Security audits: Regularly review code for potential vulnerabilities.

How does Pendo enhance vibe coding?

Integrating Pendo into the vibe coding workflow offers a powerful layer of visibility, validation, and iteration, turning AI-generated features into truly user-centered experiences. Here’s how Pendo enhances each phase of the vibe coding journey:

• User insights: Through detailed behavioral analytics, Pendo helps teams understand how users engage with newly generated features. For example, if a vibe-coded onboarding sequence has a high drop-off rate, product teams can pinpoint where users disengage and adjust accordingly.
• Feedback collection: In-app polls and surveys allow real-time input from users, giving developers the ability to iterate quickly. A product manager could launch a new AI-generated dashboard and instantly gather feedback on which metrics users find most useful or confusing.
• Usage analytics: Pendo tracks adoption and feature usage across segments, highlighting what’s working and what’s not. Imagine a scenario where a customer success team vibe-codes a new help widget—Pendo can reveal which users engage with it and whether it reduces support tickets.
• In-app guidance: AI-generated features may require some onboarding themselves. With Pendo, teams can build contextual walkthroughs or tooltips that help users adopt and understand new workflows without developer intervention.

By leveraging Pendo’s robust product analytics and feedback tools, SaaS teams can close the loop between code generation and user validation—ensuring that every AI-assisted feature is built with purpose, refined by real user behavior, and delivered at the speed of vibe coding.

Vibe coding represents a transformative approach to software development, blending human creativity with AI efficiency. While it offers numerous advantages, it’s essential to approach it with a balanced perspective, ensuring that applications are robust, secure, and user-centric. Tools like Pendo play a pivotal role in bridging the gap between AI-generated code and real-world user requirements, making vibe coding a viable strategy for modern SaaS development.