You’ve heard of low-code, maybe even used it. But there’s a new trend on the rise: vibe coding. No, low-code isn’t dead. But the Vibe Coding trend is shifting how we think about building software. And if you’re curious about what vibe coding means, how it works, and what best practices to follow, this guide is for you.
What Is Vibe Coding?
Vibe coding is the art of building software by describing what you want in plain language while an AI assistant writes the code. Instead of writing every line manually, you guide the AI with your intent. Want a Python function that reads a customer CSV file? Just say it. Want to handle missing files? Say that too. You keep iterating, describing changes, and the AI keeps coding.
Coined by Andrej Karpathy in early 2025, the term vibe coding has quickly gained traction. Even Collins Dictionary named it the Word of the Year. Platforms like ChatGPT, Claude, OpenAI’s Codex, and many others now power this kind of development.
You become less of a programmer and more of a prompter, tester, and product thinker. It’s fast, accessible, and conversational.
Two Styles of Vibe Coding
Pure Vibe Coding
This style involves trusting the AI completely without reviewing or inspecting the code yourself. You describe what you need, accept whatever the AI generates, and move on. It works well for small experiments, quick prototypes, or personal utilities where the stakes are low and rigorous testing is not critical.
Responsible AI Assisted Coding
This is the professional approach. You still guide the AI with natural language, but you actively review, test, and refine the output before shipping anything to production. It treats the AI as a collaborator rather than an autopilot. This style is recommended for serious development work because it balances speed with reliability.
Why Vibe Coding Matters?
Recent industry surveys show how quickly this approach is spreading. Tech startups report an adoption rate of approximately 73 percent, digital agencies report around 61 percent, and e commerce organizations report roughly 57 percent. Now let us look at the reasons behind this widespread adoption.
Build without being a developer
People with no coding background can create functional software. A sales manager can describe a reporting tool. A marketer can describe a customer journey. A CRM admin can describe an automation flow. The AI writes the code or configuration based on those instructions.
Faster prototyping
Ideas can take shape in minutes. Instead of starting from a blank editor, you describe a feature and the AI produces a starting point. If you want a small utility script, a simple form, or a proof of concept, vibe coding accelerates the early stages dramatically.
Easier iteration
The describe generate refine loop makes experimentation natural. If the AI misunderstood your request, you simply clarify. If something is missing, you ask for the addition. This conversational loop feels forgiving and creative compared to strict traditional coding rules.
Democratizes innovation
Business teams no longer need to wait for developer capacity. They can turn ideas into working prototypes on their own. This empowers more people to build solutions for their problems and reduces bottlenecks in IT.
Focus on the idea rather than the code
You think about what the product should do. The AI handles how it is built. The shift frees mental space and encourages quicker decision making.
How Vibe Coding Works?
So, here’s the five steps you need to follow to make sure you’re doing it right
Step 1: Describe Your Goal
You start by telling the AI exactly what you want to build or change, using clear and precise natural language instructions. Clear prompts are crucial for better quality code generation.
Example: "Build an interactive dashboard that shows monthly sales by region."
Step 2: The AI Generates Code
The AI takes your prompt and produces an initial version of the requested feature. This may include creating files, writing functions, scaffolding components, or even building the skeleton of the entire app. The output is often a basic working version that needs refinement.
Step 3: Run and Review
You execute the generated code to see how it works. The initial version may work partially or fail completely, and that's expected. Testing the output helps you understand what improvements or fixes are needed.
Step 4: Give Feedback
You provide feedback to the AI to refine the code. This feedback can involve requests such as adding new features, fixing bugs, making UI elements responsive, connecting to databases, or adding validations. The iterative conversational flow continues as you specify improvements in natural language.
Examples of feedback:
- "Add validation for the email field."
- "Make the chart responsive."
- "Connect this to my database."
Step 5: Repeat
This iterative feedback loop continues, with each cycle improving clarity, precision, and functionality. The process repeats until the code meets your expectations and is ready for use or deployment.
Vibe Coding Best Practices
The checklist below gives you a structured way to use vibe coding productively without creating unnecessary complexity or confusion.
Start With Clear Requirements
Before you generate any code, document what you plan to build. Create a simple spec or README describing the purpose of the app, who will use it, and the core features you want. This “north star” keeps prompts focused, prevents scope creep, and gives the AI a stable reference point.
A good first step is asking the AI to create a README with requirements, tech stack, and milestones before writing any code.
Begin Small and Build Up
Never dump your entire app idea into one prompt. Ask for the smallest workable piece first, test it, then add the next feature. This simplifies debugging and prevents the AI from generating bloated, fragile code.
Keep the Tech Stack Simple
Early versions should use lightweight tools. Avoid complex servers, databases, or multi-service architectures unless absolutely necessary. A minimal stack reduces breakages, speeds up generation, and produces cleaner code.
Provide Full Context
Tell the AI exactly what versions, frameworks, or libraries you want. Include links, snippets, or documentation if needed. Mention version constraints (for example, which Tailwind or React version to use) to avoid outdated or incompatible code.
Get a Plan Before Code
Ask the AI to outline its approach before writing anything. Review the plan, request simpler alternatives if needed, and set ground rules such as modular structure, file organization, or naming conventions.
A simple prompt like “Explain your plan first, no code yet” saves hours of rework.
Give Precise, Developer-Level Instructions
Treat the AI like a junior developer. Describe button behavior, input rules, data types, and user flows in detail. Specific instructions lead to accurate code; vague instructions lead to rewrites.
Use Visual Aids When Needed
If UI layout matters, include screenshots or sketches. A visual reference helps the AI understand spacing, component placement, and design intent far better than text alone.
Break Tasks Into Tiny Steps
Request one small, testable change at a time. After each change, test the output, confirm it works, and then move to the next task. Working in small increments also keeps the AI’s context clean.
Test After Every Change
Run the app immediately after the AI updates your code. Test typical cases and edge cases. If something breaks, describe exactly what you expected versus what actually happened.
Expect Breakages and Debug Actively
New features may break existing ones. When this happens, paste error messages or faulty code blocks directly into the chat. Explain the scenario that caused the issue so the AI can target the fix instead of rewriting unrelated parts.
Use Version Control and Backups
Save your work regularly. Use Git, branches, or checkpoints to store stable versions. If the AI generates problematic code, revert to the last working commit instead of trying to salvage a broken direction.
Document and Understand the Code
It is easy to end up with code you did not fully write or review. Ask the AI to explain files, add comments, or rewrite unclear sections. Maintain your README and architecture notes to keep the project maintainable over time.
Be Patient and Iterate
AI coding is iterative. You might require several prompt adjustments, tests, and corrections to get the result you want. Over time, you will learn how to prompt better and guide the model efficiently.
Know the Limitations
AI-generated code often contains hidden bugs, outdated patterns, or insecure logic. Use AI for prototypes, boilerplate, UI layouts, and repetitive tasks. For business logic, security, or performance-critical code, always review manually or refine with expert-level scrutiny.
What to Watch Out for When Vibe Coding?
Even with powerful AI tools, vibe coding is not immune to failure—especially when speed is prioritized over structure. A recent case on Hacker News (Nov 2025) details a failed attempt to build a full-scale CLI and UI system using only vibe coding prompts. Here are the key lessons to draw from that experience:
Do not over-trust the AI.
The developer relied heavily on AI to generate large portions of the system without manually reviewing the architecture. While the prototypes worked in early phases, the application became unstable as it scaled. This shows that untested AI-generated code can introduce subtle flaws that only surface under real-world pressure. Always test and validate thoroughly.
Avoid vague prompts.
Poorly defined prompts led the AI to guess at implementations, which compounded inconsistencies. Without clarity, the AI switched logic styles between iterations and introduced naming mismatches that spiraled into major technical debt. Be exact about what each function or module should do, and break requests into focused steps.
Be ready for debugging challenges.
As the project grew, debugging became nearly impossible. The AI’s responses had shifted over time, leaving a tangled codebase full of conflicting logic. The developer described it as a “jungle-like” mess. To avoid this, maintain modularity and run tests frequently. Each iteration should be treated as potentially fragile.
Manually check for security issues.
The AI did not include validation, error handling, or authentication by default. The developer did not review these gaps early enough, which became a problem once users interacted with the app. Always assume AI-generated code lacks safe defaults and add safeguards manually.
Final Thoughts
Vibe coding is not about letting AI do everything. It’s about how you approach the process. The best results come when you bring the ideas and let the AI handle the syntax.
We can help you figure out where AI fits in your product strategy, how to use it to speed up development, and how to stay focused on what really matters. Whether you’re exploring agentic AI, refining your workflow, or just trying to ship faster with a smaller team, we’re here to help you make it work.
