Vibe Coding

Vibe coding is a coding approach that relies on LLMs, enabling humans to generate working code by providing natural-language descriptions rather than manually writing in a formal programming language. It was named the Collins English Dictionary Word of the Year for 2025.

What Problem Vibe Coding Solves

What Problem Vibe Coding Solves Modern software development is often slowed down not by a lack of ideas, but by the friction between thinking and building. Developers spend significant time translating intent into syntax, wiring components together, debugging boilerplate, and navigating complex toolchains. This gap—between creative flow and technical execution—can break momentum and make coding feel mechanical rather than expressive.

Vibe coding addresses this problem by shifting the focus from rigid, step-by-step construction to a more intuitive, intent-driven approach. Instead of battling syntax and structure, developers can stay in a state of flow, rapidly shaping ideas into working software. The goal is not to replace engineering rigour, but to remove unnecessary friction—so builders can think, iterate, and create at the speed of their imagination.

The vibe coding loop

Traditionally, software development has taken the approach that solving a problem involves fully understanding it before writing a single line of code. In reality, this approach can slow progress, especially when managing complex, evolving, or poorly defined requirements. This is where the vibe coding loop comes in. Rather than treating software development as a linear process of planning followed by execution, the vibe coding loop embraces a more fluid, iterative rhythm.

At its core, the vibe coding loop is about building momentum through small, continuous steps. You start with a rough idea, turn it into a concrete form as quickly as possible, and then improve it through repeated cycles of testing and refinement. Each iteration delivers new insights, turning uncertainty into focus and gradually making the solution more robust.

This approach shifts the focus from “getting it right the first time” to “getting something working and making it better.” It’s particularly powerful in modern development situations where tools, feedback cycles, and computing resources allow rapid experimentation. Instead of being blocked by a problem’s complexity, developers can proceed with confidence, knowing that the solution will evolve as they learn.

In the following sections, I’ll break down the vibe coding loop into its core stages, showing how each step offers a faster, more adaptive, and ultimately more effective way of building software.

Intent-First Development

Intent-first development shifts the starting point of coding from how something should be built to what outcome you want to achieve. Instead of defining detailed architectures, data models, and workflows upfront, you commence by clearly expressing the goal and letting the initial implementation emerge from that intent. For example, imagine you want to build a feature that detects fraudulent transactions. Rather than immediately designing a full fraud-detection system with rule engines and ML pipelines, you start with a simple intent: “flag transactions that look unusual.” You might first implement a basic rule, such as detecting transactions above a certain threshold or originating in a different country. This maintains a focus on delivering benefits quickly while allowing the solution to evolve as one’s understanding improves.

Rapid Iteration Over Precision

In vibe coding, learning speed is prioritised over getting everything perfectly correct from the start. Instead of carefully building a flawless implementation, you create a quick version, test it, and adjust. For example, if you’re building a recommendation engine, your first pass might simply suggest the most popular items to all users. It’s not personalised or sophisticated, but it works. You then iterate—adding user-specific preferences, improving ranking logic, and improving the output based on feedback. This rapid cycle allows you to validate ideas quickly and avoid wasting time perfecting something that might not work in practice.

Progressive Refinement

Progressive refinement is the process of continuously improving a working solution through small, targeted changes. Each iteration builds on real observations rather than assumptions. Consider building a chatbot for customer support. Initially, it might respond only to a handful of predefined queries via simple keyword matching. After testing, you notice users ask more varied questions, so you refine it by introducing natural language understanding and contextual memory. Over time, what started as a basic script evolves into a sophisticated conversational system. The key is to add complexity gradually, guaranteeing each step is grounded in actual needs.

Lower Barrier for Complex Problems

One of the biggest strengths of this approach is that it renders complex problems more approachable. Instead of needing deep expertise upfront, you can begin with a simple approximation and improve it incrementally. For instance, building a real-time traffic routing system would traditionally require advanced knowledge of graph theory, optimisation algorithms, and distributed systems. With a vibe coding approach, you might start with a simple rule: “avoid routes with reported congestion.” Then you enhance it—adding live traffic feeds, predictive systems, and smarter routing heuristics. What at first seemed overwhelming becomes manageable because you’re only solving one layer of the problem at a time, gradually building toward a sophisticated solution.

Where It Can Go Wrong

Vibe coding is undoubtedly powerful, but in practice it comes with a set of technical and real-world challenges that are important to acknowledge.

Handling complex scenarios – While vibe coding works well with standard patterns, APIs, and common frameworks, it becomes significantly harder when dealing with novel or highly complex requirements. Real-world systems often involve edge cases, performance constraints, or domain-specific logic that cannot be easily approximated through iterative intuition alone. In these scenarios, the lack of upfront rigour can lead to gaps that are difficult to close later.

Quality, Performance, and Architectural Trade-offs – Vibe coding is excellent for prototyping and quickly validating ideas, but the resulting code often lacks the optimisation and structural discipline required for production systems. Performance bottlenecks, inefficient logic, and poorly defined boundaries can emerge over time. This becomes especially problematic in distributed or large-scale systems, where success depends on deliberate architectural design and carefully engineered optimisation strategies.

Debugging – One of the more subtle challenges. As solutions evolve organically without a clearly defined architecture, tracing issues can become difficult. When AI-generated or rapidly iterated code is layered repeatedly, understanding the flow of logic—and identifying the root cause of failures—requires more effort than with more structured approaches.

Maintainability – Systems built through rapid iteration can become harder to maintain if structure is not introduced at the right time. Over time, what started as a flexible, fast-moving codebase can become opaque and difficult for others (or even the original developer) to understand. This creates friction when updates, enhancements, or refactoring are needed, particularly in long-lived applications.

Security and Risk Exposure – Perhaps the most critical concern is security. Code produced quickly—especially with heavy reliance on AI or iterative shortcuts—can bypass the level of scrutiny typically applied in traditional development. Without consistent reviews, validation, and security checks, vulnerabilities can be introduced and remain unnoticed. In production environments, this creates real risk, making it essential to pair vibe coding with strong governance and review practices.

The Road Ahead

Vibe coding is still in its early stages, but it already points to something far more enduring than a passing trend. What we are witnessing is the beginning of a broader transformation in how software is conceived, built, and iterated upon.

As prompting continues to evolve alongside language models, developer skillsets will inevitably follow. The ability to clearly express intent, effectively guide AI systems, and critically assess generated outputs will become just as important as traditional programming expertise. In this shift, developers increasingly take on the role of architects and editors—shaping, steering, and refining systems rather than constructing them line by line.

At the same time, the lowered barrier to entry opens new possibilities. Domain experts, product thinkers, and non-traditional developers can now contribute more directly to building solutions, bringing fresh perspectives into the development process. This democratisation has the potential to unlock significant innovation, provided it is supported by appropriate guardrails and governance.

Ultimately, the road ahead is defined by convergence. Vibe coding and traditional engineering practices are not opposing forces, but complementary approaches. The teams that succeed will be those that can move seamlessly between fast, intent-driven exploration and disciplined, well-structured execution—building not just faster, but with greater clarity and purpose.