Being able to code something that a human understands into a working system is the goal of software development. Developers used to have to do this manually, writing every line of code, debugging, and improving performance. That is changing with rapid development of artificial intelligence (AI).

The term “Vibe Coding” was created by Andrej Karpathy and has acquired a decent amount of hype in 2025. It is the reinvented way programming is done. Developers describe what they want to accomplish using natural language, AI writes the code, and also improves it.

We’re going to discuss vibe coding in great detail, what it is, why it matters, how it works with workflows such as CI/CD, and what it means for software engineering testing and development in the future.

What is Vibe Coding?

Definition

In layman’s terms, vibe coding is an AI-enabled development method in which the developer leverages natural language prompts to define what they aspire to build. “Build a Node.js service that gets customer data from an API and stores it in a database,” for example, is what a developer might put in as a prompt. The required code is then generated by an AI model.

How it Works

  • Prompting: Wished behavior is formulated by developers in natural language. This is crucial because better input leads to better output. Clear and accurate instruction can build a solid base for the AI to riff on, but a vague prompt may end in code that doesn’t meet your needs or isn’t complete.
  • Code Generation: The AI model produces an initial code draft in response to the input prompt. This sketch could include many possible concepts, such as the data model, boilerplate, and even full functions. Such repetitions are necessary because, even though a working solution has been produced, it might not be fully optimized or follow industry best practices.
  • Iteration: After the code is written, programmers can go back to check it for accuracy of meaning and ease of readability. They verify that the AI is working properly, identify any holes, and give it targeted feedback. Between developers, this process loop improves the code until it meets the project requirements.
  • Refinement: The AI improves the solution it provides through feedback. This could be as simple as condensing the code to increase speed, removing known bugs, or adhering to coding guidelines. The end product is a production-ready product after many trials.

The Evolution Toward Vibe Coding

The foundation was characterized by the arrival of AI-based coding enablers like ChatGPT, GitHub Copilot, and Tabnine. Vibe coding is much more than just simple code suggestions or autocompletion supported by earlier models. It enables you to construct complete modules, applications, or even systems without having to write every task by hand.

This parallels broader trends in DevOps and software engineering testing:

  • Agility: Traditional testing and coding processes often lead to bottlenecks that hinder innovation. The capability to go from concept to functional prototype in a matter of mere minutes with vibe coding accelerates delivery and promotes an agile mentality. Teams are able to experiment with more flexibility in the absence of having to commit months of work up front.
  • AI in Testing and Coding: At first, AI was considered to be useful for specific coding tasks, such as detecting errors or making syntax suggestions. Its functionality is now expanding to include holistic testing and coding processes. AI works together, not just to help. As AI aids in both code generation and code testing simultaneously, the lines separating development and QA become increasingly blurred.
  • Natural Language Interfaces: in the past, using computers needed proficiency with programming languages, frameworks, and commands. By trading a conversational interface for this rigidity, vibe coding helps developers to interact in the language of intent. Natural language prompts enhance the intuitiveness and inclusivity of coding by bridging the gap between human thought and machine execution.

Benefits of Vibe Coding

Speed and Efficiency

Code that demands humans invest hours to write can be generated in seconds by AI. This leads to faster idea validation for rapid prototyping.

Accessibility

Simple prompts can be utilized by non-programmers to build working prototypes. As a result, a higher number of people can contribute to software development, democratizing the process.

Integrating with Code Testing

While vibe coding accelerates development cycles, it also puts pressure on code testing. Testing procedures must adapt to keep up with the need to validate AI-generated code.

Boosting Productivity in CI/CD Pipelines

Vibe coding aids in continuous deployment pipelines where AI-written code is automatically integrated, tested, and shipped with minimal overhead when integrated with Harness CI/CD or comparable tools.

Risks and Challenges of Vibe Coding

  1. Security and Reliability Concerns: AI can generate less-than-ideal or unsafe code. Vulnerabilities can enter production if testing coding practices aren’t adhered to.
  2. Skill Degradation: An over-dependence on AI may hinder developers’ capability for debugging and problem-solving.
  3. Black Box Problem: Debugging and maintaining AI-generated systems becomes harder if developers don’t fully understand the code.
  4. Compatibility with Software Engineering Testing: Traditional testing assumes that developers are well aware of the internal working of the system. Vibe coding challenges this by writing code that developers and testers might not fully comprehend, highlighting the importance of innovative AI-based testing.

Vibe Coding in Practice

Use cases

  • Prototyping: The accelerated building of MVPs (minimum viable products).
  • Automation Scripts: Generating small utilities or integration scripts.
  • Experimentation: Exploring new frameworks without intensive prior knowledge.

Real-World Examples

  • As per YC startups in 2025, codebases generated by AI in 25% of projects were 90% or more.
  • Vibe coding has enabled developers to swiftly produce dashboards, APIs, and even full mobile applications in a matter of days as compared to weeks.

How Vibe Coding Impacts Code Testing

From Manual to Automated Testing

To validate functionality, developers build test coding scripts in traditional workflows. Developers using vibe coding must depend on automated code testing frameworks to detect potential problems with AI-generated code.

Pairing with Vibe Testing

Although vibe coding is the main topic of this blog, it is critical to note how it functions in tandem with Vibe Testing. AI-based testing frameworks can develop test suites dynamically, ensuring alignment between development and QA, just as AI generates code.

Harness CI/CD and Testing Automation

  • By integrating AI-generated code into pipelines, teams can:
  • Teams can automatically identify changes in the application.
  • Execute end-to-end, integration, and unit tests for each commit.
  • When code is modified, test suites can be adjusted using AI.
  • Continue at a faster pace without sacrificing quality.

Future of Vibe Coding in Software Engineering

  • Mainframe Adoption: Vibe coding might become a standard component of the developer’s toolkit as tools evolve. AO assistants may finally replace IDEs and version control as the default coding environment.
  • AI-Augmented Teams: Developers are here to stay. Rather, they will function as curators, guiding AI-generated results, enhancing prompts, and ensuring accuracy.
  • Greater Emphasis on Software Engineering Testing: As vibe coding gains popularity, QA will also be modified. Effectively validating AI-written code will be just as critical as actually writing the code.
  • Regulatory and Ethical Considerations: In a world where AI generates a large segment of the code, organizations need to address issues of intellectual property, accountability, and compliance.

Best Practices for Embracing Vibe Coding

  1. Always Validate AI-Generated Code: Combine thorough testing and coding practices with vibe coding and use AI-empowered test automation tools such as testRigor.
  2. Include AI in CI/CD Pipelines: Automated validation with tools like Harness CI/CD.
  3. Maintain Human Oversight: To guarantee security and clarity, developers should thoroughly assess code generated by AI.
  4. Encourage Learning: Ensure that core software engineering capabilities are not eliminated by dependency on AI.
  5. Experiment Before Production: Before going into production, thoroughly verify your prototypes using vibe coding.

Conclusion

Vibe coding is good for those who want to build running applications quickly. Although it may seem the ideal way to go ahead with software development, it is still riddled with challenges and cannot give you the sustainable application development you get with traditional coding practices. Nevertheless, it is definitely worth a try and great for beginners looking to get more hands-on experience with development.