Vibe coding in practice: Building software with peace of mind at the core

By early 2025, vibe coding had become a hot topic in developer forums and engineering circles. At its core, it’s a conversational way of programming: you describe what you want in plain language, an LLM generates the code, and you decide whether to accept it. Instead of combing through the codebase line by line, you adjust prompts until the output fits.

At Vention, we see it a little differently. Vibe coding is a strong tool for rapid prototyping and hypothesis testing, when speed matters more than polish. But building production-grade software calls for something more. Maintainability, scalability, and security can’t be left to chance.

That’s why we treat vibe coding as a launchpad, not a finish line. It helps engineers validate ideas in minutes, then move into structured engineering practices to create reliable, long-term solutions. That balance of speed and rigor is how we make vibe coding deliver for real systems and client expectations.

We’ve seen AI hype cycles before. But in 2025, the context is different:

Long gone are the days when LLMs were autocomplete tools. GPT-5, Claude 4, and Gemini 2.5 can now process entire codebases, frameworks, and architectural logic in a single pass, making them valuable collaborators.

Assistants like GitHub Copilot, Cursor, and Claude Code are no longer bolt-ons. They’re embedded directly into IDEs, making prompting feel like a native input method.

What began with startups has reached global enterprises. Banks, SaaS leaders, and healthcare providers are running LLM workflows in secure sandboxes, a clear sign that vibe coding is moving into mission-critical environments.

Budgets are tighter and expectations higher. Organizations need to ship faster with leaner teams. Vibe coding helps scale output without one-to-one increases in headcount.

Programming once required a deep understanding of syntax and frameworks. Natural-language programming lowers the barrier, enabling product managers, designers, and QA testers to prototype directly and widen collaboration.

The software development loop has been rewritten. What once moved in the rhythm of Plan → Code → Test → Deliver now runs as Plan → Prompt → Review → Test → Deliver → Learn. AI accelerates every stage, but the key decisions still belong to people.

Teams still begin by defining the problem, desired outcome, and constraints. Human judgment is crucial in clarifying business goals, weighing trade-offs, surfacing risks, and setting non-functional requirements, such as security, performance, and scalability. Engineers also prioritize scope, select success metrics, and align stakeholders before any generation begins.

Instead of diving straight into code, developers now write mini-specs as prompts to guide their work. For example: “Build a Node.js Express microservice that fetches Salesforce Account data by website domain, caches responses, and returns a normalized JSON object.”

Models can create workable scaffolds, but they can also be confidently wrong. Engineers review the output to fit the project context, including pinning dependencies, removing anti-patterns, validating libraries, tightening error handling, and adding observability.

AI can draft unit and integration tests, but the strategy still belongs to humans. Teams decide what deserves coverage, design fixtures and datasets, address flaky tests, and determine when residual risk is low enough to move forward.

Delivery requires rigor. Engineers run code reviews, enforce CI policies, and choose rollout strategies. They validate logging, metrics, and alerting, set SLOs, prepare rollback paths and runbooks, and ensure compliance and change-management checks before anything goes live.

Finally, teams learn from what they ship. They compare prompts and outputs, review incidents and tickets, and refine prompt libraries and coding standards. Successful patterns are documented, brittle approaches retired, and guardrails adjusted so the next cycle runs faster and safer.

• Speed: Work that took days now takes hours.
• Focus: Less typing, more thinking.
• Output: More experiments per sprint, more ideas tested, more tickets closed.
• Collaboration: Prompts become shared artifacts across PMs, designers, and developers.

At Vention, we see vibe coding as a powerful capability, but one that only delivers real value in the right context. In some cases, it drives remarkable speed and efficiency. In others, it introduces unnecessary risk. Here are the situations where AI-assisted coding works best, along with the signals that warrant caution.

• Proof-of-concept apps and early startup demos
• Boilerplate code (form handlers, validation, API scaffolding)
• Utility scripts and lightweight CLI tools
• First-pass automated test coverage (unit, integration, smoke tests)
• Refactoring and cleanup of legacy functions or modules
• Framework translation or migration (e.g. React → Vue, Flask → FastAPI)
• Data transformation scripts (ETL preprocessing, CSV-to-DB pipelines)
• Filling gaps in test coverage (scaffolding, snapshot tests, mock data generation)
• Documentation and inline comments for complex modules
• Code linting, formatting, and style enforcement across repositories

Vibe coding shows its value most clearly in practice. As Ihar Mikailau recalls:

• Complex business logic with intertwined side effects or multiple dependencies
• Security-critical functions (encryption, authentication, authorization, payments)
• Performance-sensitive algorithms (real-time processing, large-scale optimization)
• Systems requiring human nuance or domain expertise (medical decisions, legal compliance)
• Highly regulated codebases that demand formal verification (aviation, medical devices, defense)
• Safety-critical systems with zero tolerance for failure (autonomous vehicles, industrial control)
• Proprietary or poorly documented APIs that require deep integration

Vibe coding is powerful, but not foolproof. As Ihar explains:

Some call vibe coding a gamble: you never know whether you’ll get something usable. I disagree. In my experience, it almost always produces a workable result. Even if it succeeded only half the time, it would still be faster than manual coding. What once required waiting until deployment to validate can now be tested in under five minutes."

Many companies misuse vibe coding. They experiment in silos, lean too heavily on automation, and mistake “code generation” for true delivery. Without structure, those efforts quickly unravel.

AI can accelerate progress just as easily as it can introduce risk if left unchecked. Once it became clear the technology was here to stay, we moved quickly to establish a company-wide framework for using it safely and effectively.

Our approach is based on two principles: every use of AI must create tangible value, and none of it can compromise client security. We view it as our responsibility to help engineers not only experiment, but also master the practice, adopting best practices, avoiding pitfalls, and scaling what works.

At Vention, we encourage our engineers to embrace vibe coding. But like any tool, it requires structure and oversight. We believe guided adoption is essential for sustainable innovation. With teams across continents and more than 3,000 experts, unguided experimentation could quickly lead to fragmented efforts, duplicated work, and unnecessary risk.

To address this, we’ve developed a company-wide AI policy that:

• Defines a vetted list of approved AI tools for security and performance
• Establishes clear procedures for evaluating and adding new tools
• Outlines principles and ethical standards for use, including data privacy, bias awareness, and transparency
• Specifies scenarios where AI use is recommended and where it is prohibited

The policy is mandatory for every Ventioneer. It enables us to move quickly without compromising safety, and it fosters a shared understanding of AI’s role in our workflows, allowing teams across locations and disciplines to collaborate effectively rather than reinventing the wheel.

In vibe coding, outcomes often depend on the quality of the prompt. At Vention, we continuously gather insights and share proven approaches to help engineers:

• Structure prompts for clear, consistent behavior
• Track prompts and completions during development to trace decisions, debug model behavior, and audit for potential IP exposure
• Set realistic expectations for model capabilities and limitations
• Request explanations, justifications, or refactoring when needed

For us, the value of AI lies in efficiency. That’s why we focus on measuring its actual effect on both individual productivity and team performance.

We encourage engineers to:

• Track how often they use AI in daily work
• Categorize the types of tasks they prompt for (e.g., code generation, debugging, documentation, research)
• Measure how often output is accepted as-is versus needing revision
• Record hours saved, bug regressions, and satisfaction with AI-augmented workflows
• Document how their workflow evolves as they gain experience with AI tools

Developers at Vention can always request licenses for AI tools and LLMs from a curated list of vetted options. Every tool on that list is approved against strict security, compliance, and performance standards, which ensures safe adoption across teams without slowing them down.

To make adoption practical, our internal learning community runs regular meetups, workshops, and knowledge-sharing sessions where engineers share real-world experiences, demonstrate use cases, and refine best practices for AI integration together.

AI is designed for efficiency and often delivers results confidently, but that confidence can be misleading. Even the most advanced models can hallucinate, fabricate details, or drift from requirements in subtle ways. We treat AI like a highly motivated junior contributor: fast, capable, and creative, but not yet reliable enough to work without supervision.

That’s why we foster a rigorous review culture across our teams. Engineers are trained to:

• Identify and flag hallucinations or fabricated data, especially in high-risk contexts
• Compare AI output against established team conventions, coding standards, and project requirements
• Take full responsibility for any AI-assisted output. “That’s AI, not me!” is never a valid excuse

At Vention, every AI-generated contribution is reviewed by a domain expert. No shortcuts. Just because it looks right doesn’t mean it is right, so scrutiny is essential.

As Ihar notes: “The biggest challenge of a software engineer in the AI era is to validate the code generated by AI. That requires even more proficiency than in the pre-AI era”.

AI adoption must align with trust, security, and responsibility. Accordingly, every team at Vention is trained not only to use AI effectively but also to use it ethically and in full compliance with client, legal, and industry standards.

Our training covers:

• What not to include in prompts, such as client data, credentials, personal information, or other sensitive material
• Model-specific risks and licensing considerations, including how different vendors handle data, retain inputs, and limit certain use cases

"Engineering peace of mind" is more than a tagline for us. It’s the standard we measure ourselves against. In the era of vibe coding and generative models, it still means stability, predictability, and craftsmanship, but it now also includes:

• Innovation without fear
• Speed with structure
• Tools that enhance rather than replace expertise

Vention clients look beyond fast delivery. What they expect (and always get) is:

• Systems that scale
• Partners who challenge assumptions
• Teams that take full ownership of outcomes

AI can accelerate delivery, but it can’t guarantee quality, chart the full roadmap, or align with a CTO’s long-term vision. That’s where Vention adds value by blending AI’s efficiency with the judgment, skill, and accountability that only experienced engineers can provide.

The pace of change makes predictions difficult, but not impossible. Ihar offers his outlook on the next breakthroughs in AI for software engineering:

“Vibe coding delivers efficiency and speed. It’s the natural next step after manual code creation, the rise of GitHub Copilot in 2022, the era of autocomplete, and now AI agents embedded in IDEs. However, I view it as only an interim stage.

The next phase is fully agent-led systems. First, specialized agents will run tasks from the console or server, with humans monitoring their execution. Beyond that lies multi-agent orchestration, where agents not only perform tasks but also oversee one another’s progress, while humans remain the ultimate context holders: setting goals, digging into generated code when necessary, and steering outcomes with judgment and intent.”

Vention partners with forward-thinking companies, from dynamic startups to Fortune 500 giants. We empower business leaders with the latest and most fitting engineering methods to innovate at speed, operate more efficiently, and move forward knowing every little detail is covered.​ ​

With 20 years of software expertise and AI-enabled delivery models, we build the right product the first time, every time, reducing time to launch and reinforcing your edge with every release.

We deliver:

• End-to-end software development teams
• Staff augmentation with elite engineers
• Strategic consulting on product, platform, and performance
• AI strategy and implementation, from discovery workshops to agent-based systems
• Cloud and DevOps services for scalable, reliable infrastructure
• Cybersecurity assessments and audits to protect sensitive systems
• Data engineering and analytics to transform information into insight
• Design, QA, and product discovery to ensure usability and quality at every stage
• …and many more services across all modern tech stacks and solutions.

What we don’t do:

• Cut corners
• Ignore process
• Chase hype without structure

Because building software is too important to compromise. And with us, you never have to.