Aug 24, 2025·8 min
Vibe Coding in Practice: How It Differs From Engineering
Vibe coding is a fast, experiment-first way to build with AI. Learn how it works day to day, how it differs from software engineering, and when it fits.
Vibe coding is a fast, experiment-first way to build with AI. Learn how it works day to day, how it differs from software engineering, and when it fits.
“Vibe coding” is intent-first building: you start with what you want to happen, try something quickly, and steer the result by feel and feedback rather than by designing every detail upfront. The “vibe” is the tight loop—write a bit, run it, react, adjust—until the product behaves the way you imagined.
At its best, vibe coding is prompt-driven development with a builder mindset: you describe the outcome, generate or write a first draft, then iterate based on what you see. It’s less “perfect plan, then execute” and more “make it real, then shape it.”
AI-assisted coding makes this approach faster because it can draft scaffolding, suggest implementations, and translate vague intent into working code. But the approach existed before today’s tools—AI just lowers the cost of trying ideas.
The core skill is still human: deciding what to build next, spotting when something is off, and keeping the iteration and feedback loop honest.
If you want an example of a workflow built around this loop, Koder.ai is essentially “vibe coding as a platform”: you describe the app in chat, iterate on behaviors and UI, and let an agent-based system generate and adjust the project (web apps in React, backends in Go/PostgreSQL, and mobile apps in Flutter). The point isn’t that a tool “replaces engineering”—it’s that it compresses the time from idea → running slice → refinement.
Vibe coding fits creator culture: people want to ship small experiments, prototypes, and personal tools without asking permission. Accessible tooling—hosted dev environments, app templates, and capable copilots—makes rapid prototyping feel normal instead of “only for experts.”
It’s not magic, and it’s not skipping thinking. You still need to scope, test, and make tradeoffs. Vibe coding also isn’t “no structure”: it’s choosing just enough structure to keep momentum while you learn what the product should be.
In practice, vibe coding feels less like “planning a system” and more like “guiding a smart pair-programmer toward a useful result.” The goal is momentum: get something working fast, then tighten it up in short loops.
Pick a small, testable outcome you can finish in one sitting—something that produces a visible result. For example: “A page where I can add items to a list and they persist after refresh.” A thin vertical slice beats a broad checklist because it surfaces real constraints early.
Before naming files or debating architecture, write what the feature should do in plain English: inputs, outputs, edge cases, and what “done” looks like. This becomes the anchor for your prompts and your evaluation.
Ask the AI to generate an initial implementation, then immediately add guardrails:
You’re not accepting code blindly—you’re shaping the search space.
Run it, break it, adjust. When something fails, feed the AI concrete signals: error messages, current behavior vs. expected behavior, and the smallest reproduction steps. Alternate between prompt tweaks and small code edits so you don’t lose control of what changed.
Maintain a lightweight “decision log” as you go: what you tried, why you changed direction, and what tradeoffs you accepted. It prevents repeating dead ends and makes it easier to hand off the project later—even if the session was improvisational.
Vibe coding and traditional software engineering can produce similar-looking outputs (a working feature, a deployed app), but they optimize for different things.
Vibe coding is biased toward motion: try an idea, see the result, adjust quickly. The goal is learning and momentum. Traditional engineering is biased toward predictability: making sure the work can be estimated, reviewed, tested, and maintained over time.
That difference shows up early: vibe coding treats the first version as a probe; engineering treats it as the start of a system.
In a vibe workflow, the “spec” is often a prompt plus a handful of examples: “Make the checkout feel simpler,” “Add a filter like this,” “Match the tone of this page.” It’s conversational and flexible.
Engineering usually translates intent into requirements, acceptance criteria, and tickets. That structure makes work easier to coordinate and verify—especially when multiple people touch the same area.
Vibe coding encourages local experiments: quick scripts, one-off components, minimal ceremony. Traditional engineering pushes toward shared patterns and architecture so the system stays coherent as it grows.
Neither is “more correct”—they just serve different constraints.
Vibe coding often stops at “it runs and feels right.” Engineering asks extra questions: Will it break under load? Is it testable? Is error handling consistent? Are edge cases covered?
Vibe coding is usually optimized for individual flow. Engineering is optimized for teams: conventions, code review norms, documentation, and a shared definition of done so progress doesn’t depend on a single person’s context.
Vibe coding shines when the goal is speed, learning, and momentum—not perfect architecture from day one. If you’re using AI-assisted coding as a partner for rapid prototyping and iteration, these are the situations where prompt-driven development tends to pay off.
If you need a demo, an internal tool, or a small feature quickly, vibe coding is hard to beat. You can describe the outcome (“a dashboard that shows yesterday’s signups and errors”) and let the model draft the first version, then refine through feedback. This is especially useful when the work is self-contained and the risk of breaking core systems is low.
When requirements are fuzzy, traditional software engineering can spend a lot of time planning for scenarios that never happen. Vibe coding lets you build a thin, workable slice, put it in front of users, and learn what matters. The “spec” becomes the result of short cycles of iteration and feedback.
A builder mindset often learns faster through making than reading. Vibe coding can help you get unstuck in unfamiliar frameworks: generating starter code, suggesting file structure, and explaining errors. You still learn the concepts, but you’re learning in context, with something tangible on the screen.
Stakeholders don’t respond to abstract descriptions as strongly as they respond to “try this.” Vibe coding is great for getting to a clickable prototype—basic flows, simple UI, seeded sample data—so product development conversations become concrete.
Tiny automations (report scripts, data cleanup helpers, simple Slack bots) are ideal. They’re usually low-ceremony, easy to test, and deliver immediate value—perfect for AI-assisted coding to accelerate.
The common thread: these use cases benefit from speed and learning. When the cost of being slightly messy is low, vibe coding gives you the fastest path to something real.
Vibe coding is great for exploring “Can this work?” Traditional engineering wins when the question becomes: “Can this keep working—predictably, safely, and with other people depending on it?”
If the feature touches payments, authentication, permissions, or anything safety-critical, speed is rarely the bottleneck. The hard part is correctness under edge cases, attack scenarios, and operational failures.
A quick AI-assisted implementation can still be valuable as a sketch, but shipping requires careful threat modeling, defensive coding, and review. In these areas, “mostly right” is often the same as “wrong.”
Systems with strict compliance or audit requirements need traceability: who changed what, why it changed, and evidence it was tested. Similarly, uptime-driven systems require monitoring, rollback plans, capacity planning, and incident playbooks.
Those needs push you toward:
As soon as multiple people contribute, shared conventions and stable interfaces matter more than individual momentum. Traditional engineering practices—API contracts, versioning, code review norms, and consistent patterns—reduce coordination costs and prevent “surprise breakage.”
For products expected to live for years, maintainability dominates raw speed. That means tests that cover behaviors (not just lines), readable modules, consistent naming, and a data model that won’t paint you into a corner.
Some bugs can’t be solved by trying variations until something works. Distributed systems, tricky business rules, performance bottlenecks, and “only happens in production” issues often require deep domain understanding and methodical investigation—classic engineering discipline.
Vibe coding looks spontaneous from the outside: you describe what you want, the AI writes code, and you keep nudging it until it works. But the real differentiator isn’t “being good at AI.” It’s being good at scoping—turning a fuzzy idea into a bounded problem the model can solve without guessing.
A strong vibe session begins with a small problem statement and a clear definition of “done.” For example: “Convert a CSV of leads into a deduplicated list by email, preserving the most recent timestamp” is solvable. “Clean up my lead pipeline” invites ambiguity.
Before you ask for code, write down—plainly—what success looks like, what you’re willing to ignore, and what must not break.
Helpful prompts read like a mini spec:
This keeps the AI from inventing assumptions you didn’t mean.
Instead of “write the code,” try: “Give me 2–3 approaches, explain tradeoffs, then recommend one.” You’ll surface choices early (quick script vs. reusable module, strict validation vs. forgiving parsing) and avoid rewriting everything later.
Request tests, example data, and failure modes. Prompts like “What inputs will break this?” or “Add tests for edge cases and show expected outputs” often catch issues before you run anything.
Treat each prompt as a small change with a single goal. When something’s off, don’t restart—tighten the spec, add one missing constraint, and rerun. That rhythm is the “vibe,” but the skill is disciplined clarity.
Vibe coding moves fast—so the goal isn’t “perfect architecture,” it’s preventing the kind of mess that makes the next change twice as hard. A little structure early keeps momentum high because you spend less time untangling surprises later.
Begin with one thin slice that works end-to-end: a single user action that flows through UI (if any), logic, and storage/API, even if it’s bare-bones. This creates a stable spine to iterate on. When you add features, you’re extending something real—not stacking half-finished parts.
Lightweight guardrails pay off immediately:
This isn’t heavyweight process—it’s insurance that lets you keep experimenting.
Keep the code easy to read and easy to regenerate: small functions, clear names, and obvious modules (e.g., api/, services/, ui/). If you can describe a file’s purpose in one sentence, you’re doing it right.
Write just enough so someone can run it without you:
Before you send a link or open a PR, run a quick checklist: remove dead code, rename confusing variables, add TODOs where you knowingly cut corners, and verify the thin slice still works. That five-minute pass is often the difference between “cool prototype” and “usable starting point.”
Vibe coding moves fast, which means quality has to be lightweight, repeatable, and easy to apply mid-flow. The goal isn’t to turn a prototype into a bureaucracy—it’s to catch the mistakes that cost you hours later.
Before you trust anything, make sure the project runs reliably from a clean state. That means a fresh install, clear setup steps, and one command that works.
If you can’t reproduce your own result, you don’t have a product—you have a lucky machine.
Don’t aim for full coverage. Add the tests that protect the core:
These tests create a safety net for further AI-assisted iterations, where a small refactor can quietly change behavior.
Generated code can be inconsistent. A formatter and linter keep the code readable without team debates. They also catch common errors (unused variables, bad imports) before you ship them.
Ask simple questions:
This is especially important when the AI suggests “quick fixes” like broad admin access or dumping debug output.
AI can echo recognizable snippets. If something looks copied (especially large blocks), replace it or confirm it’s from a permissive source. When in doubt, keep it original and documented.
Vibe coding can feel casual—fast prompts, fast results—but the moment the code touches real users, the responsibility is yours. “AI wrote it” doesn’t change who is accountable for security, correctness, legal compliance, or harm.
Treat prompts, chat history, and pasted snippets like production artifacts. They can be stored, reviewed, exported, or accidentally shared.
When an assistant generates code, you often don’t know what it resembles. That uncertainty matters.
Be explicit about sources when you do borrow code (from docs, GitHub, Stack Overflow). Avoid copying “unknown origin” snippets into a product without review. A simple habit helps: add a short comment with the reference link when you intentionally adapt something.
AI-generated logic can encode assumptions: names, addresses, currencies, gender, language, disability needs. Test with diverse inputs and users—especially for flows like onboarding, payments, moderation, and eligibility.
Vibe coding is excellent for rapid prototyping, but prototypes can look deceptively finished. Tell stakeholders what’s real and what’s placeholder: security hardening, monitoring, performance, and legal review may not exist yet. A one-line label in the README (“demo quality”) can prevent expensive misunderstandings.
A vibe-coded prototype is great at proving a concept, but teams need more than “it works on my laptop.” The goal is to preserve the speed you gained while making the work legible, testable, and owned.
Package the prototype like you’re passing a baton, not a mystery box. Write a short “README for humans”: what the feature does, how to run it, what’s mocked, what’s hard-coded, and which parts are experimental. Include a quick demo script (steps + expected output) so others can validate behavior in minutes.
If you built the prototype on a platform like Koder.ai, take advantage of the practical handoff features: export the source code, capture a snapshot before major changes, and keep a simple rollback path so early experiments don’t become irreversible.
Your prompts are useful history, but tickets need clarity. Convert the prototype’s intent into:
If you still have the original prompt thread, paste key excerpts into the ticket as context—not as the spec.
In early productionization, reviewers should prioritize:
Style can follow once the risks are controlled.
“Done” typically means: reliability targets, basic monitoring/alerts, minimal docs, and a clear on-call/ownership path. If nobody owns it, it’s still a prototype.
Refactor when the core design is sound but messy. Rewrite when the prototype’s structure blocks testing, performance, or security. A good rule: if you can’t explain the architecture in a few sentences, pause and redesign before piling on features.
Vibe coding clicks with a generation that grew up learning by doing: watching a short tutorial, trying it immediately, and sharing results fast. When an idea can turn into a working demo in an hour, the distance between “I have a concept” and “I built something” shrinks dramatically—and that changes who feels allowed to build.
AI-assisted tools remove a lot of early friction: boilerplate setup, syntax anxiety, and the “blank file” problem. That doesn’t mean hard problems disappear, but it does mean beginners can start with outcomes—an app that runs, a feature that works—and learn the details along the way.
Vibe coding fits naturally with tight iteration loops: prompt, run, tweak, repeat. You get immediate signals from the product itself—does it feel right, is it useful, is it confusing? That speed makes learning more playful and less punishing than weeks of planning before seeing anything.
Many new builders aren’t aiming for a “perfect” system on day one. They want to ship tiny tools, share them, and iterate based on real reactions. Vibe coding supports that approach because it’s optimized for momentum: you can test ideas like experiments rather than committing to a long build.
Instead of translating intent into rigid instructions from the start, you can describe what you want in normal language, refine it with the tool, and steer toward the result. For many people, that feels closer to brainstorming than “programming.”
The craft shifts from memorizing APIs to making good calls: what to build next, what to simplify, what to delete, and when the output is “good enough” for the goal. In vibe coding, taste—plus the willingness to iterate—becomes a real technical advantage.
Vibe coding shines at discovery: turning a fuzzy idea into something you can click, test, and react to. Traditional engineering shines at durability: making that thing reliable, understandable, and safe to change. The trick is not choosing one—it’s knowing when to switch modes.
Explore (vibe-first): sketch the feature with quick prompts, accept messy code, and optimize for learning. Keep a “parking lot” note for things you’re knowingly skipping (auth, edge cases, error handling).
Validate (reality check): run the app, try dumb inputs, and confirm the core flow works. If it’s not meaningfully better than the alternative, stop early—this is where vibe saves time.
Harden (engineering pass): refactor into clear modules, add tests around the most valuable behavior, and make failures obvious (good errors, safe defaults). Write down assumptions and trade-offs so future you isn’t guessing.
Maintain (team-friendly): document how to run it, how to deploy it, and how to change it without breaking everything.
If you want vibe speed without chaos, learn the basics of debugging, testing, and security hygiene (input validation, auth boundaries, secrets handling). That’s enough to keep momentum while avoiding avoidable breakage.
Next steps: improve your prompting workflow with /blog/how-to-write-better-prompts-for-coding, and if you’re evaluating tools or plans, check /pricing.
It’s an intent-first way of building software: start with the behavior you want, generate or write a quick first version, then iterate in tight loops based on what you see running.
A good vibe session is less about “no rules” and more about “fast feedback + just enough structure to keep control.”
No—AI makes it faster, but the workflow (build a slice, test, adjust) existed long before copilots.
AI mainly reduces the cost of trying ideas by drafting scaffolding, suggesting implementations, and helping you debug—while you still own the decisions.
Start with a tiny, testable outcome you can finish in one sitting.
Example: “A page where I can add items to a list and they persist after refresh.” That thin slice exposes real constraints early without committing to a big architecture.
Write a mini-spec in natural language:
Then use that as the anchor for prompts and for judging whether the result is actually correct.
Give concrete signals:
Avoid restarting from scratch; tighten one constraint at a time so you can see what changed and why.
A decision log keeps fast iteration from turning into repeated dead ends.
Keep it lightweight—just bullets like:
It also makes handoff and later cleanup dramatically easier.
Vibe coding optimizes for speed and exploration; engineering optimizes for predictability, coordination, and long-term maintenance.
In practice that means:
Strong fits include:
The common thread: the cost of being a bit messy is low, and learning speed matters.
Use traditional engineering discipline when correctness and safety dominate speed:
A vibe-coded version can still be a sketch—but shipping needs review, tests, and threat modeling.
Use lightweight, repeatable checks that don’t kill momentum:
If you want a simple blend routine, use: explore → validate → harden → maintain.