Vibe Coding Versus Vibe Learning: Building A Solid Foundation For Product Engineering

The rise of vibe coding—where developers depend heavily on AI to write their code—makes the time before 2010 highly relevant. Back then, the industry was defined by one type of developer: the programmers! Their characteristics? Deep understanding of memory, OS, data structures and algorithms. Their tools? Bare-bones editors, command-line mastery and handcrafted architecture.

Then came the era of stack builders, also known as the drag-and-drop developers. They knew where to click, what plug-in to install and how to build and ship an MVP in a weekend.

Fast forward to now: AI-augmented developers are taking over. They may use one AI tool to write their boilerplate and another to write their logic. They code by conversation, debug by suggestion and prototype in minutes. There’s nothing wrong with this—it’s fast and gets the job done.

But here’s the challenge: Vibe coding is a casual approach. While it gets the job done, I’ve found it often leads to non-maintainable, surface-level codebases that lack depth, scalability and sound engineering principles.

I think we can counter this through code automation fueled by a vibe-learning mindset. This is a holistic approach, where we make thoughtful adjustments in the software development life cycle (SDLC), from architecture to testing frameworks, to ensure high-quality code output. Because engineering is not just about speeding things up; it’s also about engineering excellence at scale.

Vibe coding is fast, flashy and feels like magic. Many leaders see it as the silver bullet to uplift the developer experience, as it frees them from boilerplate code and gives them more time to solve real problems.

While vibe coding is excellent for idea-to-prototype development, I don’t suggest this practice for prototype-to-product or prototype-to-market coding.

Why? Because when we pursue new approaches without proper AI hygiene and combine them with existing outdated SDLC, the core engineering pillars often begin to wobble. Here’s how vibe coding can impact these core pillars:

The Aftermath Of Skipping Core Engineering Pillars

Great software isn’t just built—it’s engineered. When we give the cold shoulder to core engineering pillars and chase short-term velocity, results may get replaced by consequences, and consequences may get replaced by chaos.

Define architecture-level rules that AI-assisted coding must comply with (naming conventions, layer separation, dependency rules).

Focus on creating cloud-native solutions that can accommodate rapid iterations and AI-based optimizations.

Prioritize long-term maintainability by using design principles like SOLID. Also, make sure that AI-generated code adheres to the same quality standards as manually written code.

Leverage AI-assisted tools for first-level code reviews, but ensure manual oversight from experienced developers.

Establish a clear, standardized code review framework.

Encourage feedback from cross-functional teams.

Integrate peer reviews as a mandatory step, and track code quality using metrics.

Increase test coverage to catch edge cases and ensure high-quality outcomes.

Implement AI-driven testing tools for regression and automated checks.

Integrate AI tools within a CI/CD pipeline to ensure that automated testing runs seamlessly during every code push.

Conclusion

Vibe coding is all about making things just work. Whereas vibe learning emphasizes understanding the why and how behind it, going in depth and making necessary adjustments throughout the SDLC to embrace this new approach to coding.

Because speed with structure, creativity with correctness, automation with alignment and engineering with excellence are what make human-AI collaboration meaningful and fruitful.

Originally Published on: Forbes.com