Vibe Coding Examples: 6 Practical Projects to Explore

Introduction

Vibe coding makes it possible for almost anyone to describe an idea in plain language and get back a working first version, favoring speed over the extensive engineering process a traditional build would require. Knowing where to actually start, though, is a different problem, especially for teams used to submitting a ticket and waiting on a build.

Vibe coding can take shape as a quick visual mockup, a working prototype, or a small proof of concept, depending on what you're trying to learn. This article walks through six examples professionals can try. They're illustrative projects anyone could build, not finished or commercial products.

6 Practical Vibe Coding Examples for Business Teams

Using AI tools for vibe coding can quickly generate runnable code and app components based on simple user descriptions, but it's important to familiarize yourself with the process before launching anything. These six examples, the kind of projects people run into at work, move from simple and visual to more involved, closer to something with real data and workflow logic behind it. Each one starts with the business need, walks through a sample prompt and possible first output, and ends with what to check before going any further.

1. Visualize a landing page before building it

Turning a page concept into something a team can react to usually means waiting on development time nobody has. Vibe coding skips that wait. A single prompt can generate a working layout with real hierarchy and flow, giving a team something concrete to discuss rather than a written brief that people interpret differently.

Sample prompt. "Build a landing page for small business owners considering [product]. Include a hero section with a headline and CTA, three benefit cards, a testimonial section, and a pricing comparison table. Make the CTA button sticky as the visitor scrolls."

Possible first output. A single working page with real layout and spacing, even if the copy and images are placeholders, is enough to react to hierarchy and flow before it ever reaches a first user.

What to check next. Approved copy, accessibility review, brand guidelines, and actual implementation still follow. A clickable mockup answers whether the structure works, not whether it's ready to publish.

2. Test an interactive product selector

Complex product ranges are hard to browse. Helping customers land on the right option usually means a long list of filters or a lot of guesswork. A prompt can produce a guided quiz flow in minutes, asking a few questions and pointing customers toward a recommendation before anyone touches the real catalog.

Sample prompt. "Build a product selector web app that asks three questions about use case, budget, and space available, then recommends one of five product types with a short explanation for each recommendation."

Possible first output. A working quiz flow with placeholder recommendation logic, enough to click through every path and see where an answer leads.

What to check next. This tests the interaction, not the accuracy of the recommendations. Confusing questions or dead-end paths show up fast. The real product data, recommendation logic, and inventory availability still need separate verification before the tool touches the live catalog.

3. Explore a dashboard with sample data

Not every reporting idea needs a full data pipeline before anyone knows if the layout actually helps. A prompt can assemble a dashboard built entirely from invented records, one that stakeholders can click through and respond to right away.

Sample prompt. "Build a dashboard using sample data for 50 support requests. Include summary cards for open, in-progress, and resolved counts, a filter by priority and assigned team, and a table showing average turnaround time by category."

Possible first output. A working dashboard that stakeholders can filter and react to immediately, using invented records instead of anything pulled from a live system.

What to check next. A convincing dashboard built on sample data hasn't addressed where live data will come from, whether it's fed by a real database, how often it updates, or whether the numbers actually mean what people assume.

4. Prototype a supplier onboarding workflow

Supplier intake often lives across email threads, spreadsheets, and whoever remembers the last step. Nobody quite sees how broken it is until someone maps it out. A prompt can spin up clickable screens covering intake, status tracking, and approval, letting procurement and operations walk through the whole process together for the first time.

Sample prompt. "Build a supplier onboarding flow with a submission form for company details and required documents, a status view showing where each submission stands, and an approval screen for reviewers to accept or reject a request with comments."

Possible first output. Screens procurement and operations can walk through together to spot missing steps or unclear handoffs before committing to anything real.

What to check next. A lasting version requires agreed-upon data structures, real access rules for each role, document handling that meets any applicable compliance requirements, and connections to the systems that already track suppliers and payments.

5. Mock up a self-service account experience

Customers shouldn't have to call someone just to review an order or update an account setting. Building a real account infrastructure to test that assumption is a lot to commit to upfront. A navigable set of screens, built on sample data, can answer the question of whether people can actually find what they need long before that infrastructure exists.

Sample prompt. "Build a customer account experience with a dashboard showing recent orders, a page to update contact and payment preferences, and a support request form with a status tracker, using five sample customer accounts."

Possible first output. Screens that a real user can click through using invented account data, enough to test whether the layout and labels actually make sense.

What to check next. This tests whether customers can find information and complete a task without help. Real identity verification, permissions, and connections to the actual account system, along with anything that touches sensitive data, are a separate body of work that the prototype doesn't cover.

6. Check an integration idea with a small proof of concept

Two systems rarely agree on how data should be structured. Finding that out mid-integration is expensive. With a narrow-scope proof of concept, a developer can build a small function that transforms sample data from one system into the format another system expects, answering the question in an afternoon instead of a week.

Sample prompt. "Given this sample JSON response from [system A], write a function that transforms it into the field structure [system B] expects. Handle missing fields by leaving them blank and flag any field that doesn't match the expected type."

Possible first output. Working, client-side transformation logic that handles the sample input correctly, tested against a handful of example records rather than a live connection.

What to check next. A successful sample doesn't verify how the live APIs actually behave, how credentials like API keys get stored and rotated, what happens when a request fails, or who maintains the mapping once source systems change.

What Can You Learn From Trying Vibe Coding Examples?

Each of these examples teaches you something different, but the underlying lesson is the same. You find out whether an idea holds up once it exists as something real, not just a description in a meeting. Describing what you want in natural language to an AI agent, reviewing what comes back, and prompting again until it works is how you get there.

The real value goes beyond the first prompt. You see the actual interface or behavior it produces. You also learn what needs to change once someone reacts to it. Sometimes that reaction points to the tool itself. People often start with one tool for a fast visual pass, then move to something like Cursor or Claude Code once the underlying code needs real attention. Vibe coding tools and low-code platforms take genuinely different approaches here, one built around prompts and code, the other around visual configuration.

None of the examples above are finished or production-ready. They're a starting point. What you learn from the first version determines whether it's worth taking any further.

How Do You Choose a Vibe Coding Project to Try?

Six different directions are a lot to choose from. The right project depends on what you're actually trying to learn. A few questions help narrow it down:

• What are you trying to learn? A page layout, a user flow, a reporting view, a workflow, or a technical connection each calls for a different kind of prototype, since different tasks call for different kinds of proof.
• Can you start with sample data? Use information that lets people evaluate the idea without accessing live business records, so the experiment stays low-stakes.
• Who needs to react to the result? Whether the example is for customers, business stakeholders, designers, or developers changes what "useful" actually looks like.
• What would make the experiment useful? Identify one specific decision it should inform, like whether a workflow is worth developing further at all.

This isn't meant to turn into a full security review. It's worth taking seriously, though. Research from Veracode found that 45% of AI-generated code samples contain security flaws. A quick check now beats a costly one later.

What Happens When an Example Becomes a Lasting Application?

Choosing the right prototype to build is only the first decision. What happens afterward matters just as much. Some prototypes answer their question and can be retired right there. Others reveal a workflow the business actually wants to keep using.

Take the supplier onboarding example from earlier. Once people start relying on it regularly, the team needs reliable records, clearly defined permissions, a real approval process, working integrations, and someone to own it going forward. These are the same requirements any operational workflow needs, whether or not one of today's AI models wrote the first version.

Liferay DXP's low-code capabilities can support a lot of that. Objects handle structured data, Forms handle intake, Workflows handle approval steps, and permissions and integrations connect it to the systems already in place, effectively a governance layer around what started as a quick prototype. Specialized functionality may still call for custom development and its own review, especially when a team needs to add features that no off-the-shelf capability covers or wants more control over exactly how something behaves. The point isn't that Liferay DXP built the original prototype. It's about choosing the right foundation once a prototype becomes something the business actually depends on.

Start With the Idea You Need to Test

Start with a narrowly defined question, not a broad request to build an app. AI tools handle boilerplate code fast, which is part of why the first output, however rough, gives you something to react to and refine right away. That reaction is usually more useful than the prototype itself. Share it as a quick post for feedback, or keep it private, whatever gets you an honest reaction fastest.

An example's real value is making an idea testable, whether or not it ever becomes a permanent application. If one of these experiments becomes a workflow your organization needs to run and maintain, explore Liferay DXP's low-code capabilities to see what a governed foundation could look like.

Frequently-Asked Questions About Vibe Coding Examples

Where did the term "vibe coding" come from?

AI researcher Andrej Karpathy popularized the term in 2025, describing a way to build software by working from what you want rather than writing code line by line. The name stuck because it captured something real about how quickly an idea can turn into a working first version.

What are some simple vibe coding examples for beginners?

A landing page mockup, a basic calculator, simple browser games, or a dashboard using sample data are all good starting points. None of them requires a coding background to attempt. Each has a narrow goal and gives you something visible to review and refine rather than an abstract idea. Start with one feature instead of asking to create a complete application.

Can you vibe code an internal business tool?

Yes, and it's often better to start there, as internal tools have a higher tolerance for rough edges. An internal request tracker or a reporting dashboard could easily start as a side project or a vibe-coded prototype. "Internal" doesn't automatically mean low risk, though, once the tool touches company data or becomes part of a regular workflow. The team still needs to review access, reliability, and ownership before depending on it day-to-day. Whatever gaps the review finds still need to be fixed.

What should you include in a vibe coding prompt?

Specify who will use the project, what task it should support, the information or sample data it needs, and what should happen when users interact with it, all in plain English rather than technical detail. Include important rules or edge cases where relevant. You can always refine the result through follow-up prompts after testing the first version.

Are vibe-coded examples ready to use with real customer data?

No. AI-generated code often requires a professional review before launch. A working prototype doesn't establish that its authentication, permissions, data handling, or integrations are ready for customer use. Start with synthetic data, then go through proper engineering and security review before connecting anything to live records.