Vibe Coding in Production: Can Prompts Build Real Software?
The development world is abuzz with something that would have sounded absurd just a few years ago. You sit down at your computer, describe what you want in plain English, and minutes later, you have working software. This isn't science fiction anymore, it's vibe coding. But the real question is: can prompts truly create production-ready software?
From legacy RPG systems being modernized into Angular and .NET applications to entire codebases transformed by AI, the impact is undeniable. Yet, vibe coding takes this further. It’s not just about converting existing code but about creating entirely new applications through natural language conversations with AI.
Let’s begin by exploring the basics in this blog.
What Exactly Is Vibe Coding?
Computer scientist Andrej Karpathy coined the term "vibe coding" in February 2025, describing it as "fully giving in to the vibes, embracing exponentials, and forgetting that the code even exists". But what does this really mean for software builders?
At its core, vibe coding is a shift from traditional line-by-line programming to an intent-first approach. Instead of wrestling with syntax, data types, and implementation details, you focus on describing what you want the software to accomplish in plain English. Next, AI tools like GitHub Copilot, Cursor, Claude, Replit, and more, then generate the actual code.
This represents a fundamental change in how we think about software development. With traditional coding, you're the architect, implementer, and debugger all rolled into one. With vibe coding, you become more of a product manager and reviewer. You need to set direction, provide feedback, and ensure the AI-generated code meets your needs.
Next, learn how AI coding is setting the vibe to drive efficiency.
The Promise of Vibe Coding: Speed That Changes Everything
The efficiency gains from vibe coding are staggering. Recent data shows that AI now generates 41% of all code globally, with 256 billion lines of AI-generated code written in 2024 alone. Moreover, early adopters report development speed increases of 20% to 75%, with some claiming even more dramatic improvements. Massive, right?
What used to take weeks of careful planning, coding, and testing now feels like a cakewalk for a couple of days or even hours. The key insight is that vibe coding eliminates much of the cognitive overhead that traditionally slows down the development process.
Take game developer Pieter Levels as an example. Levels built a fully functional multiplayer game that generated $1 million in annual revenue -all in just 17 days using vibe coding techniques. Or consider the startups creating complex business automation tools that allow non-technical users to generate custom scripts simply by describing their needs.
Stories like these are becoming the norm. Each day brings fresh headlines of breakthroughs - and guess what? - This is only the beginning!
Production Stories Powered by Vibe Coding
Here are some real-world examples where vibe coding is already making waves:
Gaming & Prototyping: Developers are rapidly building functional applications for entertainment, education, and simple business processes.
Healthcare: Startups are leveraging vibe coding to create wellness tracking applications without requiring deep algorithmic expertise.
E-commerce: Platforms are generating product listings and vendor tools using simple natural language commands.
EdTech: Educational technology companies are developing interactive learning platforms where students can learn programming by simply describing what they want to build.
One of the biggest impacts of vibe coding is the rise of citizen developers. That is, people without coding skills can now help build applications.
However, for every success story, there are sobering realities about what happens when vibe coding meets the harsh demands of production software.
Keep reading to learn about the complexities.
The Hidden Complexities of Vibe Coding
When implementing vibe coding, here are the key challenges that often arise: 1. Security Vulnerabilities AI tools are trained on massive collections of publicly available code, and not all of it follows modern security standards. As a result, the code they generate can unintentionally carry over common risks such as SQL injection, weak authentication, or unsafe file handling. The challenge grows when less experienced practitioners use these outputs, as critical vulnerabilities may slip through unnoticed until they cause real damage.
2. Technical Debt Technical debt piles up fast when developers rely on AI-generated code they don’t fully grasp. What seems to work in the moment can later turn into a maintenance headache. As AI has no real sense of business logic, compliance rules, or project nuances, the output often functions on the surface but falls short of long-term architectural needs.
3. Maintainability Problem
Perhaps the biggest challenge with vibe coding in production is long-term maintainability. Human developers understand the business logic embedded in their applications and structure code for easy modification. However, AI cannot replicate this long-term thinking.
Imagine this scenario: Six months after vibe-coding an app, adding a feature or fixing a bug can be tough. The original prompts are forgotten, the AI logic is hard to follow, and without documentation or oversight, you’re left reverse-engineering your own code.
This creates a paradox: vibe coding can make you incredibly productive in the short term while potentially creating long-term technical debt that's expensive to resolve. Meaning, the advantage of speed can quickly disappear when maintenance cycles begin.
Here’s a simple diagram highlighting where vibe coding excels—and where it runs into a few hurdles:
Where vibe coding works the best:
Rapid prototyping and MVP development
Simple business automation tasks
Educational and gaming applications
Data processing and transformation scripts
User interface generation and styling
Where vibe coding struggles:
Complex algorithmic logic
Performance-critical applications
Systems requiring strict security compliance
Large-scale architecture decisions
Integration with complex existing systems
Despite these challenges, I believe vibe coding can work in production environments—but only with the right approach.
Making Vibe Coding Work in Production
Successful production implementations require several critical practices such as:
1. Thorough code review remains essential. Every AI-generated block of code needs human review for security, logic, and architectural alignment. Static analysis tools like SonarQube should be integrated into your development workflow to catch vulnerabilities and code quality issues automatically.
2. Clear ownership and accountability must be established. Developers need to understand and take responsibility for the AI-generated code they incorporate. 3. Progressive deployment strategies can help minimize risk. Start with shadow deployments, then gradually roll out to larger audiences.
4. Version control for prompts is crucial. Just as you version your code, you need to track the prompts and contexts that generate it. This creates an audit trail and makes it easier to understand why certain decisions were made.
Let’s move on to learn about the business perspective on vibe coding.
Vibe Coding at Scale: How Are Businesses Adopting It
Businesses are adopting vibe coding, but with more caution than the hype suggests. Most of them are using a hybrid approach.
That is, mixing vibe coding with traditional development, low-code, and no-code tools depending on the need.
The business world recognizes that vibe coding excels at certain tasks- rapid prototyping, simple automation, and exploratory development—while traditional development remains essential for complex, mission-critical systems.
Moreover, businesses are putting governance frameworks in place to manage AI-generated code at scale, with measures like automated security scans, compliance checks, and quality gates to keep flawed code out of production.
What's becoming clear is that successful vibe coding isn't about replacing human developers—it's about elevating their role. The most valuable developers in the vibe coding era are those who can architect systems, understand business requirements, and provide the strategic thinking that AI cannot replicate.
Plus, prompt engineering is emerging as a key development skill, as the quality of AI-generated code depends on clear prompts and a strong grasp of both the problem domain and how AI interprets instructions.
Bonus: Build smarter products faster with Nitor Infotech’s GenAI-powered engineering.
Here’s to what’s next!
Looking Forward with Vibe Coding
We're moving from an era where coding skill was the primary bottleneck to one where problem definition and system thinking become the limiting factors.
This doesn't mean traditional coding becomes obsolete. Rather, it means that routine implementation work increasingly gets automated, freeing developers to focus on higher-level concerns: architecture, user experience, business logic, and strategic technical decisions.
The future likely holds a spectrum of development approaches such as: - Simple applications and prototypes will be primarily vibe-coded. - Complex, mission-critical systems will use traditional development with AI assistance. - - Most real-world applications will fall somewhere in between, using hybrid approaches that leverage the strengths of both methodologies.
Let’s wrap it up!
The Bottom Line of Vibe Coding
So, coming back to the question - can prompts build real software? The evidence suggests yes, but with important caveats. Vibe coding can absolutely create functional, valuable applications. This isparticularly for well-defined problems with clear requirements and limited complexity.
But building production-ready software requires more than just functional code. It requires security, maintainability, performance, architectural soundness, and most importantly, it needs humans in the loop
For developers, this means embracing vibe coding as a powerful tool while developing the complementary skills—prompt engineering, system architecture, and AI collaboration—that make it effective.
So, the question isn't about whether prompts can build real software. It's whether we can build the processes and practices to make them do it well.
Loved the read and want to build your next big breakthrough product powered by AI? Please contact us at Nitor Infotech, an Ascendion company.
Meet the author of this blog: Yash Patil, Software Engineer at Nitor Infotech.