Vibe coding is a style of software development where a person describes what they want in plain language and an AI model generates the code to make it happen. Instead of writing syntax line by line, the developer or non-developer steers the process through natural conversation, reviewing and refining the output rather than authoring every instruction manually. Bloom Group keeps a close eye on developments like this because they reshape how teams build software and what skills matter most. Below, we unpack the most common questions surrounding vibe coding so you can form a clear picture of what it is, how it works, and where it fits.
How did vibe coding originate?
Vibe coding emerged in early 2025 when Andrej Karpathy, a prominent AI researcher and former OpenAI co-founder, used the term publicly to describe a workflow he had adopted: letting a large language model write most of the code while he focused on intent and direction rather than implementation details. The phrase caught on quickly because it named something many developers were already doing informally.
The concept became possible only once large language models reached a quality threshold where their generated code was reliable enough to be useful without constant correction. Tools like GitHub Copilot, Cursor, and similar AI-assisted editors had been building toward this moment for several years. Karpathy’s framing gave the practice a memorable name and sparked a broader conversation about whether coding as a craft was fundamentally changing.
How does vibe coding actually work in practice?
In vibe coding, a person types or speaks a description of what they want to build, and an AI assistant generates the corresponding code. The human then tests the output, describes what needs to change, and the AI iterates. The loop continues until the result matches the original vision. Writing code by hand is optional rather than required.
In a typical session, someone might type something like “build a login form that validates email addresses and shows an error message if the password is shorter than eight characters.” The AI produces working code, the person runs it, notices the error styling looks off, and asks the AI to fix it. This back-and-forth replaces the traditional process of consulting documentation, writing functions, and debugging syntax errors manually.
The quality of the output depends heavily on how clearly the person communicates their intent. Vague prompts produce vague results. The skill shifts from knowing how to write code to knowing how to describe a problem precisely, evaluate whether the generated solution is correct, and catch issues the AI may have overlooked.
What’s the difference between vibe coding and traditional software development?
The core difference is who writes the code. In traditional software development, a trained engineer authors every line, applying knowledge of languages, frameworks, algorithms, and system design. In vibe coding, an AI writes the code and the human directs, reviews, and refines it. The human role shifts from author to editor and product thinker.
Traditional development demands deep technical fluency. A developer needs to understand memory management, data structures, security vulnerabilities, performance trade-offs, and the specific quirks of the languages and tools in use. Mistakes at the code level are the developer’s responsibility to find and fix.
Vibe coding lowers the barrier to producing working software but does not eliminate the need for judgment. Someone still needs to understand whether the AI’s solution is secure, scalable, and maintainable. A person with no technical background can use vibe coding to build a prototype, but deploying that prototype in a production environment serving real users requires the same engineering rigour that has always been necessary.
Who is vibe coding actually for?
Vibe coding is most useful for people who want to build functional software quickly without investing years in learning to code, and for experienced developers who want to accelerate repetitive or boilerplate-heavy tasks. It serves a broad spectrum of users, but the value it delivers differs significantly depending on the context.
- Non-technical founders and entrepreneurs can use vibe coding to build prototypes and validate ideas without hiring a developer immediately.
- Product managers and designers can create working demos that communicate their vision more concretely than a static mockup.
- Experienced developers can offload routine tasks, generate scaffolding, and move faster through familiar problem types.
- Researchers and analysts can build small tools and scripts to automate workflows without becoming full-time programmers.
Where vibe coding is less appropriate is in high-stakes production systems where security, compliance, and reliability are non-negotiable. In those environments, the AI-generated code still needs thorough review by engineers who understand what they are looking at.
What are the risks and limitations of vibe coding?
The main risks of vibe coding are security vulnerabilities, hidden technical debt, and a false sense of completeness. AI models generate code that looks correct and often runs without errors but may contain subtle flaws that only surface under specific conditions or at scale.
Security is a particular concern. An AI may generate code that is functional but leaves an application open to common vulnerabilities such as SQL injection, improper authentication handling, or exposed sensitive data. A person using vibe coding without a security background may not recognise these problems until they cause real harm.
Technical debt accumulates quickly when generated code is accepted without scrutiny. The AI optimises for producing something that works now rather than something that is easy to maintain, extend, or hand off to another developer later. Over time, a codebase built entirely through vibe coding can become difficult to reason about.
There is also a skill gap risk at the organisational level. Teams that rely heavily on AI-generated code without building underlying engineering knowledge may find themselves unable to diagnose or fix problems when the AI’s output falls short.
Is vibe coding the future of software development?
Vibe coding is almost certainly part of the future of software development, but it is unlikely to replace the discipline entirely. The more accurate picture is that it becomes a standard layer in the developer’s toolkit, changing who can build software and how quickly, while the need for engineering judgment and technical oversight remains.
In 2026, the pattern emerging across the industry is that AI-assisted development accelerates output significantly for experienced teams while enabling non-developers to contribute meaningfully to early-stage product work. The ceiling for what a small team can build in a short time has risen considerably.
What changes is the profile of skills that matter most. Deep syntactic knowledge of a single language becomes less of a differentiator. Systems thinking, architectural judgment, security awareness, and the ability to evaluate AI output critically become more valuable. The best developers of the coming decade will likely be those who combine strong conceptual foundations with the ability to direct and audit AI-generated work effectively.
How Bloom Group Helps with Vibe Coding and AI-Driven Development
Navigating new development paradigms like vibe coding requires more than curiosity. It requires the right team to assess what fits your context, implement it responsibly, and ensure the output meets production standards. That is where we come in.
At Bloom Group, we bring together developers with academic backgrounds in Computer Science, AI, Mathematics, and related disciplines who understand both the potential and the pitfalls of AI-assisted development. We help organisations:
- Evaluate where vibe coding and AI-assisted tools genuinely accelerate delivery in their specific context
- Establish code review and quality assurance processes that catch the security and maintainability issues AI tools can introduce
- Build or extend development teams through our Team as a Service model, combining AI-savvy engineers with strong foundational expertise
- Guide greenfield projects from concept through to production-ready software, applying modern development methods responsibly
- Support scale-ups and enterprises in adopting new tooling without accumulating technical debt that slows them down later
Whether you are exploring how AI development tools fit into your roadmap or need experienced engineers to lead a complex build, we are ready to help. Get in touch with us and let’s talk about what your next project needs.
Frequently Asked Questions
What tools should I start with if I want to try vibe coding for the first time?
If you are just getting started, Cursor and GitHub Copilot are two of the most accessible entry points. Cursor is particularly well-suited to vibe coding because it allows you to describe changes in plain language directly within the editor and applies them across your codebase. Start with a small, low-stakes project — a personal tool or internal script — so you can get comfortable with the prompt-refine loop before applying it to anything critical.
How do I write better prompts to get more useful code from an AI?
The most effective prompts are specific about inputs, outputs, constraints, and context. Instead of asking for 'a user login system,' describe the exact behaviour you need: the fields involved, the validation rules, the error states, and the tech stack you are working in. Including examples of what success looks like — or what you have already tried — also helps the AI produce more targeted results. Think of it less like issuing a command and more like briefing a capable colleague who needs full context to do their best work.
Can vibe coding be used safely for production applications, or is it only suitable for prototypes?
Vibe coding can absolutely contribute to production-grade software, but the AI-generated output should never go to production without a thorough review by an engineer who understands security, performance, and maintainability. The practical approach most teams use is to treat AI-generated code the way they would treat a junior developer's pull request: useful, often impressive, but requiring careful scrutiny before it ships. Establishing a code review process specifically designed to catch the vulnerabilities and shortcuts AI tools commonly introduce is essential if you are building anything user-facing.
What are the most common mistakes people make when adopting vibe coding in a team environment?
The most common mistake is accepting AI output at face value because it runs without errors. Code that executes is not the same as code that is secure, scalable, or maintainable. Teams also frequently skip documentation and testing when using AI-generated code, assuming the AI's output is self-explanatory — it rarely is at the architectural level. A third pitfall is using vibe coding as a substitute for engineering knowledge rather than a complement to it, which can leave a team unable to debug or extend their own codebase when the AI's suggestions fall short.
Does vibe coding work with any programming language or framework, or are some better supported than others?
Current AI coding tools perform best with widely used languages and frameworks that have large amounts of publicly available training data — JavaScript, TypeScript, Python, React, and similar mainstream technologies tend to produce the most reliable results. Less common languages, niche frameworks, or highly proprietary codebases will generally yield lower-quality output because the underlying models have seen fewer examples to learn from. If your stack is less mainstream, expect to spend more time reviewing and correcting generated code, and consider providing the AI with additional context about your conventions and architecture.
How should non-technical founders evaluate whether the code an AI generates is actually good?
Non-technical founders should focus on three practical checks: does the application behave exactly as described under normal and edge-case conditions, has the code been reviewed by at least one qualified engineer before it handles real user data, and does the person or team responsible for the project understand what the code does well enough to explain it. Running automated security scanning tools such as Snyk or SonarQube can also surface common vulnerabilities without requiring deep technical expertise. For anything beyond a proof of concept, bringing in an experienced developer for even a brief audit is a worthwhile investment.
Will relying on vibe coding hold back developers who are still early in their careers?
This is a genuinely important concern. Developers who skip the foundational stage of writing and debugging code manually may find they lack the mental models needed to evaluate AI output critically, diagnose subtle bugs, or make sound architectural decisions. The most effective approach for early-career developers is to use AI tools as a learning accelerator rather than a shortcut — asking the AI to explain its output, experimenting with modifying generated code by hand, and deliberately practising the fundamentals alongside AI-assisted work. Strong conceptual foundations will become more valuable, not less, as AI tools become more prevalent.