AI-assisted coding uses large language models to translate natural language prompts into code. Automating repetitive and boilerplate tasks shortens development cycles and reduces labor costs. However, companies are increasingly turning to specialized firms to rescue AI-generated codebases that lack proper architecture and security safeguards, reflecting growing pains in the rapidly expanding vibe coding market.
Why is this cleanup crisis happening when the technology itself works well? Companies are treating AI coding as a magic solution that anyone can use, when it actually requires senior-level expertise to guide it properly. As more companies realize their internal teams lack the expertise to use AI coding effectively, they are outsourcing to professional software firms like Belitsoft — a development company with over 20 years in engineering and AI integration — that apply structured, expert-led workflows to ensure quality, security, and maintainability in AI-assisted projects and can reduce previously high development costs for clients.
Benefits of Vibe Coding
For example, engineers can build a production-ready library in several days using an AI assistant, a task estimated to have taken weeks or months otherwise. This is a 2–5× speedup in development time.
CEOs report that using AI coding tools save them the equivalent of thousands of developer hours of effort. Even Google’s CEO confirms that over 30% of new code at Google is now AI-generated.
This leads to lower development costs. Industry studies found that companies integrating AI into their software process achieved about 30% savings on software production costs on average.
With senior engineers supervising the workflow, the productivity gains do not compromise code quality or security.
Experienced developers can use AI to handle rote coding tasks while ensuring the output meets high standards. This means far fewer billable hours are needed to deliver a given scope of work, so the total cost of ownership for clients plummets even as quality is maintained (and often improved).
Enterprise surveys show rising client satisfaction as projects are delivered faster and with solid reliability.
Rather than replacing senior developers, AI amplifies their output: a skilled engineer working with LLM pair programmers can produce much more at the same time.
One FAANG engineer noted that about 50% of his daily code is now AI-generated, freeing him to provide the broader brush strokes and review the code, and allowing him to resolve certain issues 10× faster than before.
By offloading tedious boilerplate (like unit tests and data-handling functions) to AI, developers focus on higher-level design and bug prevention.
The result for clients is more value for less money – projects that have required 1000+ developer-hours in the past can be delivered with only a few hundred hours of human work, thanks to AI assistance.
As a consequence, some tech giants have even begun reducing total engineer headcount or reassigning junior roles, given that many repetitive coding tasks have been automated. (Notably, new hires with less than 1 year experience fell, as entry-level work is increasingly handled by AI.)
Nonetheless, companies that empower their senior developers with these AI tools are seeing enthusiastic teams delivering more value faster.
When experienced engineers remain at the helm, they can retain full accountability for the end product – ensuring the software is high-quality and secure – while letting the AI handle the grunt work.
This senior-driven approach has led to visibly happier developers and satisfied clients, as projects finish in half to a third of the time and within a fraction of traditional budgets, all with robust results.
Methodology & Internal Process for the Right Vibe Coding
Realizing the above benefits requires more than just throwing an AI at some code.
Successful vibe coding has a secret sauce: a methodology that front-loads the project with solid engineering practices before any AI writes code.
The team invests extra effort in a solid technical specification and architecture design up front, which then makes the downstream AI prompts extremely effective.
Coding without solid planning and engineering practices can be a real vibe-killer – if you rely on an LLM without a clear design, the results can be chaotic.
Architecture & Requirements First
Senior engineers and business analysts collaboratively produce a detailed technical spec, clear requirements, and an architectural blueprint for the system. This blueprint is often explained in plain language (for stakeholder buy-in) and covers everything from data models to how the app will be deployed in different environments, as well as how third-party APIs will be safely integrated.
By having this blueprint, the team ensures the AI works toward a cohesive vision rather than ad-hoc code. Some AI-assisted development tools enforce this separation of phases: they have a "Plan Mode" for discussing architecture and design with the AI, and only then an "Act Mode" to generate code once a plan is approved. This human-in-the-loop planning prevents the AI from coding hallucination.
Modular, Iterative Prompting
Instead of one gigantic prompt asking the LLM to build an entire application (which often fails due to context limits and complexity), the work is broken into modules or features.
For each component, developers provide specific prompts (backed by the spec) describing the one module’s functionality in detail. The LLM then generates that code.
This iterative approach – treating the LLM like a programmer who implements one task at a time – yields far better results than a single "mega-prompt".
It also allows continuous refinement: the developer can test each AI-produced module, catch errors, and adjust prompts for the next module accordingly.
AI Code Generation & Senior Review
Once the prompt is given, the LLM writes the code. Every single line of AI-written code is then inspected, tested, and corrected by a senior engineer before it gets merged.
This review step is critical: the AI can produce working code quickly, but only a seasoned human can verify nuances, ensure the code fits the overall design, and enforce standards.
Seniors often catch subtle issues or suboptimal approaches that the LLM, lacking true understanding, might miss. LLMs can introduce new kinds of bugs – code that looks polished but fails in edge cases – which an experienced reviewer needs to sniff out.
By having seniors in the loop to debug and polish, the final codebase remains clean and maintainable. Even a routine feature like a user login, which might have taken a developer 16–40 hours traditionally, can be specced out and generated by the AI in minutes – but it’s the senior engineer’s careful validation and tweaks that guarantee it meets all security and quality requirements before a Git push.
Continuous Testing and Integration
The workflow integrates testing at each step.
Test cases (which can themselves be drafted by AI) are run on AI-generated code to ensure it meets the spec. Any failing tests or runtime errors are fed back to the LLM in a prompt (much like an error message to a dev) so it can suggest a fix.
This loop repeats until the module passes all tests. Modern AI coding tools support such cycles.
This approach results in code that is well-documented and aligned with requirements, with the AI effectively doing a form of test-driven development under human guidance.
This methodology – design first, then small prompt cycles, with constant human oversight – make the coding phase up to 10× faster than normal in the experience of some teams. By spending more time in design and prompt preparation, the actual coding becomes almost automatic.
Senior developers experimenting with these techniques accomplish in 4 days what would normally take two weeks, by letting AI generate code and only later stepping in to assist with tricky bugs and integration. Multiple AI agents in parallel (several AI instances working on different feature branches, with one coordinating merges) can pump out an astonishing 10,000 lines of code per day across projects. Careful planning (writing thorough specs, tests, and even style guidelines for the AI to follow) is a key to keeping that output high-quality – recreating an architect’s oversight in an automated loop.
Security and architecture are never left to the AI by itself
Senior engineers incorporate dedicated security checks and wrappers around things like third-party API calls, rather than assuming an AI will do so by default.
Naive use of LLMs (just asking for a feature implementation without mentioning security) often yields code that misses common security best practices.
In one evaluation, all popular LLMs tested produced code with serious vulnerabilities (like mishandling input or hardcoding secrets) when given an unspecified prompt. Only when the prompts included explicit security requirements or when additional rule-based guardrails were added did the AI outputs become secure.
The human team must bake in security and sound architecture from the start. An experienced architect mandates an extra validation layer around an external API integration – even if the AI didn’t spontaneously suggest one – to protect the overall system.
When companies like Belitsoft audit AI-generated code from external projects, they often find that a lack of upfront architecture/design review leads to major gaps that require costly rework. Outsourced devs did not spend one second beyond the spec sheet – no extra thinking about edge cases or "common sense" improvements – resulting in a product that technically met requirements but wasn’t a cohesive, maintainable system. That entire codebase had to be partially rewritten and fortified after the fact.
By contrast, a well-run vibe coding process avoids these pitfalls by designing in quality and security from the beginning (through explicit prompts, policies, and senior reviews).
Market Demand for Vibe Coding
The rise of vibe coding reflects a broader surge in demand for AI-assisted development.
AI-powered code generation has become one of the hottest areas for investment and innovation in tech. Venture funding and valuations for startups in this space have skyrocketed.
Reuters reports that code-gen startups are commanding sky-high valuations as companies race to adopt AI in software development. These tools allow users to translate plain English commands into code, sometimes called vibe coding in the press. Such tools promise that even non-programmers can create working software by simply describing what they need, while experienced developers can dramatically accelerate routine work.
On the demand side, companies of all sizes are experimenting with LLM-driven development. Many companies had at least begun adopting generative AI technology in their business, and software development is a prime use case.
Many startups have fully embraced vibe coding – 25% of startups may have 95% of their code generated by LLMs. These young companies lean heavily on AI to crank out an MVP quickly.
However, this rush often leads to technical debt: observers note that some founders using AI produce low-quality code, as cheap and dirty as they can, with no long-term planning. As a result, there’s a growing niche for firms with senior AI expertise to step in and "rescue" half-finished AI-coded projects.
Belitsoft and others report being approached by teams that attempted vibe coding internally but ended up with shaky systems – missing robust architecture or security safeguards – and now need seasoned engineers to audit, refactor, and productionize the work.
This scenario is becoming common as the hype meets reality: companies realize that skipping the architecture phase or lacking seasoned oversight can lead to fragile software that doesn’t scale or comply with compliance needs.
In terms of broader market trends, LLM code quality has been steadily improving, which further fuels adoption. Each new model brings better understanding of coding contexts and fewer errors. Upgrading from an older AI model may double coding speed-up (from a 1.5× boost to well over 2×) because the newer model could handle prompts more accurately. Some LLMs are top performers in generating secure code when given proper guidance.
Belitsoft continuously evaluates these models and adopts the best-performing ones for each task – whether it’s using code-specific models for certain languages or switching to a larger context model for handling big codebases. The effect is a virtuous cycle: as the tools get better, more organizations are willing to pilot them on real projects, which in turn drives more innovation and investment in the space.
The enterprise sector, however, has been slower to fully embrace vibe coding compared to startups.
Large enterprises often have legacy codebases, strict compliance requirements, and established development processes – all of which can act as "crutches" slowing down AI adoption.
There is also a talent gap: many corporate developers and managers are not yet adept at crafting high-quality prompts or integrating AI agents into their workflows. Prompt engineering and AI oversight are becoming new skills that enterprises must cultivate.
That said, even conservative organizations have started dipping their toes into AI-generated code, particularly for new, self-contained modules or for modernizing legacy systems.
A notable example is Morgan Stanley, which built an internal tool to help rewrite its old COBOL code – effectively using an LLM to interpret and regenerate decades-old code faster than a human could. The result showed promise, confirming that LLMs can accelerate even traditionally slow enterprise development tasks like legacy refactoring. Similarly, Microsoft has deeply integrated AI into its dev pipeline (reporting 20–30% of new code being AI-generated) and even flattened parts of its workforce in response, indicating they expect existing teams to deliver more with AI assistance.
Observers predict that in the near future, even large enterprises will routinely let vibe coding handle greenfield projects or new components, while human experts focus on integration and high-level design.
This is already hinted at by internal developers: one FAANG engineer noted that while AI isn’t yet good at higher-level design choices or bigger picture stuff, it excels at pumping out well-encapsulated functions and boilerplate, so day-to-day software engineering is going to look very different once adoption becomes mainstream.
We’re seeing the beginning of this shift. Industry surveys of CIOs show a sharp increase in budget for AI developer tools and training in 2025, and early enterprise pilots are expanding. Still, full enterprise uptake will likely be gradual – many organizations are proceeding cautiously, running AI coding in sandbox environments or on non-critical projects until issues like data privacy, regulatory compliance, and staff training are addressed.
Risk Management with Vibe Coding
LLMs are powerful tools but can be useless – or even dangerous – in unskilled hands. Effective vibe coding is very much a senior-level discipline.
It requires an experienced engineer to keep full responsibility for the software, guide the model thoughtfully, catch AI errors, and enforce coding standards. Without that human expertise, an AI can churn out code that is syntactically correct but architecturally unsound or riddled with subtle bugs.
As an analogy, think of an LLM as a highly efficient junior developer: it can produce a lot of code quickly, but it has zero real understanding or accountability.
The lead engineer treats a large language model as it would a junior developer. The engineer gives concise instructions and runs every draft through review. The model delivers scaffolding in minutes, so the engineer’s own development time drops.
The less experienced developers take a different approach. They paste the model’s suggestions straight into the codebase and move on. They do not pause to consider architecture or security trade-offs. From a distance, their productivity looks impressive — one day of work now produces as much code as a full week had before.
An LLM can write convincing code that is still wrong, and developers without enough experience often miss the difference. When the lead engineer opens their pull requests, the engineer sees defects which take time to explain. Output volume has increased, but so has the lead engineer’s review load. Progress now depends on the engineer’s ability to detect and explain subtle problems the LLM has introduced.
Security is another area where experience is critical.
LLMs do not inherently understand secure coding practices - they’ll often omit important steps (like input validation, authentication checks, or proper error handling) unless explicitly told.
A junior might not even realize those omissions. Researchers found that naive AI prompts (asking for a feature without security detail) caused every tested LLM to produce code vulnerable to multiple common weaknesses. This means if an unskilled developer just says "Build me a login form" and copies the AI’s output, they might deploy code that is susceptible to SQL injection or other attacks.
A senior engineer, on the other hand, will either prompt the AI with specific security requirements or manually add the necessary checks around the AI’s code. They understand the gotchas and ensure nothing critical is overlooked. Many senior-led AI projects incorporate a manual review for security: in high-risk areas (payments, authentication, third-party APIs), the code might even be hand-written or heavily refactored by the senior to guarantee safety, rather than relying on the AI’s first draft.
The contrast in outcomes has led to a saying in these circles: LLM coding isn’t bad – bad developers using LLMs are. In other words, the rumor that vibe coding = garbage code arises mainly from cases where organizations let loose AI tools without proper training or oversight.
The real issue is insufficient expertise guiding the AI, not the AI itself. When steered by a skilled hand, vibe coding can produce clean, robust code, but in unskilled hands, it can amplify poor practices (mass-producing flawed code at an overwhelming pace).
As one early adopter put it after using multiple AI agents on a complex project: this tool would absolutely ruin a codebase if you weren’t a very experienced dev and tech lead. Without a solid grasp of software architecture and best practices, a developer could quickly inject chaos by trusting the AI too much. Another commenter predicted we’ll be swimming in an ocean of bad software written by inexperienced devs leaning on AI, while good experienced devs will be able to make better software with the same tools.
This underscores the point: human role differentiation is key. Juniors must still learn the fundamentals and should use AI carefully (to learn or speed up small tasks, but not design entire systems). Seniors, meanwhile, take on more of a governance and orchestration role – they treat the AI like a helpful junior engineer who needs supervision. They remain accountable for the final product, guiding the AI with high-quality prompts (encoding their expertise into the instructions) and rigorously reviewing output. With this division, the LLM becomes a force multiplier for the senior developer, not a replacement for them.
In terms of risk management, forward-looking teams are establishing policies to keep AI coding in check. This includes code review protocols (any AI-generated code must be tagged and gets an extra manual review), security scanning of AI contributions, and rules for what types of tasks are appropriate for AI.
Some companies integrate tools that automatically inspect AI-written code for known vulnerability patterns.
The consensus is that LLMs should be used with trust, but verify. They can draft 90% of a solution, but a human must verify that last 10% (and often that final polish is critical, as an uncaught AI mistake could be catastrophic).
When managed properly, LLMs are an incredibly potent aid, when mismanaged, they can accelerate the creation of bugs or security holes. Thus, effective vibe coding teams invest in training developers on how to use AI responsibly, pair newcomers with seniors as mentors, and maintain a culture where using an AI doesn’t mean skipping diligence. The engineer – not the AI – remains the ultimate owner of the code quality.
Outcomes of Vibe Coding
Despite the challenges, the on-the-ground outcomes of senior-driven vibe coding have been largely positive – even exciting – for many developers.
Those who have incorporated AI into their workflow report significant productivity boosts on the tedious parts of coding, and a better overall developer experience. Quantitatively, the reported gains vary, but they are often substantial.
Overall productivity improvements of 20–50% are common once developers get used to AI assistance. In specific scenarios (particularly repetitive, boilerplate-heavy tasks), the speed-up can be much more dramatic – on the order of 5× to 10× faster.
One engineer noted that writing unit tests or parsing routines, which might have taken him half a day, became trivial with AI: things that would have taken me 10× time otherwise were solved quickly by the model. Another developer found that using the latest LLMs with an optimized workflow doubled his output (a solid 2× boost), and he occasionally saw even higher throughput on easy, well-represented tasks where the AI excelled.
Beyond the numbers, the sentiment among many developers using vibe coding is enthusiastic. They often describe the experience as transformative or liberating. Tedious boilerplate work feels like less of a burden, allowing them to focus on creative and complex aspects of software.
As one Reddit user put it, it really removes a lot of drudgery – tasks like slogging through unit tests or boilerplate code are no longer where developers derive their value. Instead, they can spend more time on architecture, refining requirements, or tackling tricky problems, with the AI handling the rote stuff.
Developers have called these AI coding tools pretty cool and express excitement about having them in their toolbox.
The initial fear some had (of AI replacing programmers entirely) is giving way to a more nuanced view: augmentation rather than replacement. Many now see AI assistance as a must-have tool. "I’m excited about AI as a great tool in our toolbox," one senior engineer wrote, adding that he’s tired of doom-and-gloom scenarios and instead sees concrete advantages. Another developer commented that these tools "help me get ideas out of my head and into something practical very quickly, as long as you can filter out some of the nonsense it returns". The AI might not be perfect, but it kickstarts progress rapidly, and with a bit of human steering, it leads to faster results.
When vibe coding is done under proper conditions (solid specs, senior review, etc.), the resulting code quality has proven to be excellent. There is growing evidence that AI-generated code, when guided well, can be as good as or even better than hand-written code for many use cases. For example, the AI output often strictly follows style guides and patterns it was trained on, meaning consistency can be very high.
In cases where minor fixes or refinements were needed, the team found it was a reasonable trade-off – the AI saved them a mountain of initial work, and the small percentage of sloppy output could be quickly deslopped by hand. This aligns with the broader sentiment that when guided by seniors and anchored to a solid architecture, vibe coding yields excellent results — even if minor fixes are still required.
It’s also worth noting the morale boost these tools provide. Developers often find it awesome to see an AI quickly generate boilerplate. It’s like having a supercharged pair-programmer who never gets tired. This makes the development process more enjoyable.
In interviews and online discussions, many express that once they got used to AI assistance, they wouldn’t want to go back. Despite initial skepticism, a lot of devs now say AI coding assistance is already absolutely necessary in their workflow.
At the same time, experienced voices caution against over-reliance or premature hype. They point out that the fundamentals of software engineering – understanding the problem, devising an elegant design, and maintaining code over time – still matter as much as ever. In other words, writing code was never really the hardest part - understanding and evolving it is.
LLMs have drastically reduced the effort of writing new code (the marginal cost of adding software is approaching zero), but they haven’t removed the need to think deeply about that code. Recognizing this, the most successful teams use vibe coding as a force multiplier for human creativity and diligence, not a replacement for them.
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