DEV Community: Remy B.

How to Vibe Code Your First SaaS (Step-by-Step)

Remy B. — Thu, 23 Apr 2026 13:30:00 +0000

Key Takeaways

Vibe coding lets you describe features in plain language and AI writes the code

Two paths: AI app builders (Lovable/Bolt) for speed, or AI coding tools for full control

A feature spec + architectural context = consistent, production-ready output

You can ship your first SaaS feature in a single session using the workflow in this guide

You can vibe code a SaaS in an afternoon. You can also spend that afternoon iterating on a dashboard Claude keeps redesigning from scratch — because your prompt was six words.

This is the step-by-step workflow I wish I'd had my first week. No specific tool required, no framework assumed. New to the concept? Read What Is Vibe Coding? first for background.

The first time I tried to vibe code a SaaS dashboard, I gave Claude Code a single sentence: "Build me a dashboard." Forty minutes later I was on my third complete rewrite — different layout, different data model, different component names each time. I closed the terminal, opened a notes file, and wrote six sentences: route, data sources, existing components, acceptance criteria, auth, layout wrapper. Twelve minutes after I pasted those six sentences back in, the feature was done and shipping. The spec wasn't overhead. It was the whole trick.

What You Need Before You Start

Before you write a single prompt, get these five things in place. None of them take more than an afternoon, and skipping any of them will cost you time later.

An idea you can describe in one paragraph. You don't need a business plan. You need to be able to say: "I'm building X for Y people, and the first thing it does is Z." If you can't describe it simply, AI can't build it well.
Version control (GitHub). Create a GitHub repository before writing any code. Every change is tracked, you can undo mistakes, and it's required for deployment. It's free — no excuses.
A hosting platform. Vercel (best for Next.js), Netlify, or Railway. All have generous free tiers. You'll deploy from your GitHub repo — push code, and your site updates automatically.
An AI coding tool. Claude Code for terminal-first agentic workflows, Cursor or Windsurf for IDE-integrated development. Pick one to start — you can always add more later.
A project foundation. A starter kit or boilerplate with authentication, payments, and database already configured. Building this from scratch takes weeks and is the wrong use of your time when vibe coding for beginners.

Once these are in place, you're ready to start.

Step 1: Write a Feature Spec (Not Just a Prompt)

This is the single biggest differentiator between people who succeed with vibe coding and people who struggle. Don't jump straight into prompting. Write down what you want first.

A feature spec isn't a full product requirements document. It's 5–10 sentences that describe: what the feature does, who uses it, and what "done" looks like. It forces you to think before you prompt — and gives AI the clarity it needs to generate useful code on the first try.

Here's the difference between a vague prompt and a feature spec:

Vague Prompt

"Build me a dashboard."

AI will generate something — but it won't be what you wanted. You'll spend more time iterating than you saved.

Feature Spec

"Create a user dashboard page at /dashboard. Show the user's name from the session, their current subscription plan from Stripe, and a list of their 5 most recent projects with title, status, and last-modified date. Use the existing DashboardLayout component. Add a 'New Project' button that links to /projects/new. The page should be server-rendered and require authentication."

The difference is specificity. When AI knows the route, the data sources, the existing components, and the acceptance criteria, it generates code that actually fits your application. This is how to vibe code effectively — not with better AI, but with better inputs.

The Quick Path: Start with an AI App Builder

Before diving into the full workflow, it's worth knowing there's a faster option — with trade-offs.

AI app builders like Lovable and Bolt can generate a working application from a text description. You describe your SaaS, and they produce a deployed app with UI, database, authentication, and basic functionality — sometimes in minutes.

This path works well for:

Validating an idea quickly before investing more time
Building prototypes to show investors or early users
Non-technical founders who need a working version fast

The trade-offs are real, though. Customization is limited. Complex features hit walls. You're on their hosting, their infrastructure, their ecosystem. When you outgrow the builder — and most serious SaaS products do — migration is painful and sometimes impossible.

If you want full control over your codebase — production-ready architecture, custom features, your own hosting — keep reading. The rest of this vibe coding tutorial walks you through doing it with AI coding tools.

Step 2: Set Up Your Project Foundation

You can't vibe code into a blank folder effectively. AI needs existing patterns to follow — file structure, naming conventions, component library, API patterns. Without them, every prompt generates code in a different style, and your project becomes an inconsistent mess within a week.

You have two options:

Use a starter kit — A production-ready boilerplate with authentication, payments, database, and infrastructure already configured. This is the fastest path.
Set up manually — Initialize a Next.js (or other framework) project, add your ORM, configure authentication, wire up payments. This takes 1–2 weeks for a solid foundation but gives you full control from line one.

What matters is consistency: a predictable file structure, shared type definitions, reusable components the AI can reference. The difference between vibe coding a prototype and vibe coding production software is the foundation underneath.

Step 3: Give Your AI Tool Context About Your Project

This is the step most beginners skip — and the one that separates good AI output from generic AI output.

Every AI coding tool supports some form of project context file: AGENTS.md for Claude Code, .cursorrules for Cursor, .windsurfrules for Windsurf. These files tell the AI about your project's patterns before it generates code.

At minimum, include:

Your tech stack and framework versions
File and folder naming conventions
Key components and utilities the AI should reuse
Patterns to follow (e.g., "server actions go in src/actions/")

Example Context File (AGENTS.md)

Tech stack: Next.js 15, TypeScript, Prisma, PostgreSQL, Tailwind, shadcn/ui.
Components live in src/components/. Pages in src/app/.
Server actions in src/actions/ — always validate with Zod schemas.
Use the existing Button, Card, and DataTable components from our UI library.
All database queries go through Prisma — never raw SQL.

With context in place, AI generates code that matches your project's conventions instead of inventing its own. This is the foundation of structured vibe coding — and it's what makes vibe coding viable for production.

Step 4: Vibe Code Your First Feature

You have a spec, a foundation, and context. Now it's time to actually vibe code. Here's the workflow, step by step.

1. Share your feature spec with the AI

Open your AI tool and give it the feature spec you wrote in Step 1. If you're using Claude Code, paste it directly. In Cursor or Windsurf, open the composer/chat and share the spec along with any relevant files.

2. Let the AI propose a plan

Don't let AI start writing code immediately. Ask it to propose an implementation plan first: which files it will create or modify, what approach it will take, which existing components it will use. Review the plan before saying "go ahead."

3. Let it generate the code

Once the plan looks right, let AI write the code. For multi-file features, agentic tools like Claude Code will create and modify multiple files in one pass. IDE tools may handle it in stages.

4. Review what it produced

Before accepting anything, check:

Does the file structure match your project's conventions?
Did it reuse existing components or create unnecessary duplicates?
Are types correct? Are imports pointing to real files?
Does the feature actually work when you run it?

5. Iterate through conversation

AI rarely gets it perfect on the first pass — and that's fine. The power of this vibe coding tutorial is showing you that iteration is the workflow, not a failure.

Iteration Prompt

"The dashboard page works, but two things: move the subscription status into a separate card component, and add a loading skeleton while the projects list fetches. Also, the 'New Project' button should use our primary Button variant from the UI library, not a plain anchor tag."

Be specific. Reference file names, component names, and exact behaviors. The more precise your feedback, the more accurate the next iteration.

Note: If you find yourself giving the same feedback repeatedly — "always use our Button component," "add loading states to all data fetches" — encode it into a reusable skill or subagent. AI tools like Claude Code support custom skills that run the same review checklist every time, so you stop repeating yourself and your code stays consistent automatically.

Step 5: Review, Test, and Ship

Don't skip review just because AI wrote it. AI-generated code compiles, passes basic tests, and looks reasonable — but it can also introduce subtle bugs, security issues, and pattern inconsistencies that compound over time.

Before you merge or deploy, run through this checklist:

Logic check. Does the feature actually do what the spec says? Test the happy path and at least one edge case.
Security basics. Are inputs validated? Are database queries parameterized? Are auth checks in place?
Pattern consistency. Does the code follow the same patterns as the rest of your project? Or did AI invent a new approach?
Quality gates. Run your linter, type checker, and any tests you have. Ask AI to write tests for the feature it just built — it's good at this.

Note: Use AI for testing too. Connect a browser automation tool like Chrome DevTools MCP to your AI agent, pair it with a testing skill, and let it click through your feature, check layouts at different screen sizes, and flag visual or functional issues — before you even open the browser yourself.

Once everything passes, commit, push, and deploy. If you set up Vercel or Netlify in Step 1, pushing to GitHub triggers an automatic deploy. Your feature is live.

Worried about AI code quality at scale? Read our data-driven analysis on vibe coding's scaling problem →

3 Mistakes That Slow Down First-Time Vibe Coders

After watching dozens of developers learn how to vibe code, these are the patterns that waste the most time:

Prompting without a spec. You describe something vague, AI generates something vague, you spend 30 minutes iterating to get what you could have specified in 2 minutes of writing. The spec is the shortcut.
No project context. Without context files, AI generates generic code that doesn't match your patterns. You end up with three different button styles, two API patterns, and a file structure that doesn't match anything else in the project.
Accepting everything without review. AI is confident, not correct. It will generate code that looks right, runs without errors, and has a subtle auth bypass or a missing edge case. Always review the diff before accepting.

Every one of these mistakes is recoverable. But avoiding them from the start means you spend your time building features, not fixing AI's assumptions.

What Is Vibe Coding? A Developer's Guide (2026)

Remy B. — Fri, 17 Apr 2026 22:30:35 +0000

I've been vibe coding for the past year — building a full SaaS product almost entirely through AI conversation. Some of it has been shockingly productive. Some of it has been a mess. This post is everything I wish I'd known when I started.

Vibe coding is the practice of building software by describing what you want in natural language and letting AI write the code. Instead of typing syntax line by line, you have a conversation with an AI tool — and it generates working code based on your intent.

If you've heard the term but weren't sure what it actually means, how it works, or whether it's something you should learn — this guide covers everything I've learned about vibe coding in 2026.

What Is Vibe Coding?

Vibe coding is a software development approach where you describe the software you want to build in plain English (or any natural language), and an AI coding tool generates the source code for you. You guide the process by reviewing the output, giving feedback, and iterating — much like directing a collaborator rather than writing every line yourself.

The term captures something specific: instead of thinking in syntax and data structures, you think in outcomes. You focus on what the software should do, and the AI handles how to implement it. The "vibe" is the shift from implementation detail to creative intent.

The Origin of "Vibe Coding"

The term was coined by Andrej Karpathy — Tesla's former head of AI and a founding member of OpenAI — in a now-famous post on X in February 2025:

There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.

Karpathy was half-joking. He described himself building a project by talking to an AI, accepting suggestions, running the code, and fixing issues through more conversation — without ever carefully reading the generated code. It captured a feeling that thousands of developers already recognized: AI had reached a point where you could build real software just by describing what you wanted.

What started as a tongue-in-cheek observation went viral. Within months, "vibe coding" went from a meme to a genuine methodology. Developer communities adopted it. Tutorials appeared. Tool makers optimized their products around it. By mid-2025, it was the dominant way new developers were learning to build software — and by 2026, even experienced engineers have integrated vibe coding workflows into their daily practice.

Vibe Coding vs Traditional Development

	Traditional Development	Vibe Coding
How code is written	Manually, line by line	Described in natural language, AI generates it
Skill required	Deep programming knowledge	Problem definition + code literacy for review
Speed	Variable, depends on complexity	Faster for standard patterns, similar for novel problems
Who it's for	Professional developers	Developers + technically literate builders
Output quality	Consistent with developer's skill	High for isolated tasks, variable at scale
Scaling behavior	Conventions maintained by team knowledge	Requires architectural context to stay consistent

The key insight: vibe coding doesn't replace programming knowledge — it changes where that knowledge is applied. Instead of writing code, you're reviewing it. Instead of memorizing syntax, you're defining intent.

How Vibe Coding Works in Practice

If you've never tried it, consider this section a compact vibe coding tutorial. Whether you're exploring vibe coding for beginners or you're an experienced developer learning how to vibe code, the core cycle is the same.

The Vibe Coding Workflow

Every vibe coding session follows a four-step loop:

Describe — Tell the AI what you want in plain language. "Build a user settings page with name, email, and notification preferences."
Generate — The AI writes the code: components, API routes, database queries, styling — whatever the task requires.
Review — You look at what it produced. Does it match your intent? Does it work? Are there obvious issues?
Iterate — Refine through conversation. "Move the notification toggles into a separate section. Add email validation." The AI updates the code accordingly.

This loop happens fast. A feature that might take a day in traditional development can often be built in under an hour. The more specific your descriptions, the better the output — but even vague starting points often produce surprisingly usable results.

Vibe Coding Example: A Pricing Page

Imagine you're building a SaaS application and need a pricing page. In traditional development, you'd write JSX, style components, wire up state management, and connect to your payment provider. With vibe coding:

Your prompt: "Build a pricing page with three tiers — Free, Pro, and Enterprise. Each tier should show the price, a list of features, and a CTA button. The Pro tier should be visually highlighted as the recommended option. Use our existing design system and connect the buttons to our Stripe checkout flow."

The AI generates a complete, functional pricing page. You review it, tweak the copy, adjust the feature lists, and iterate until it matches your vision. The entire process might take 20 minutes instead of a full day.

Essential Vibe Coding Tools

The vibe coding ecosystem has matured rapidly. Here are the main categories of tools and when each type shines.

Vibe Coding with Cursor and Windsurf

Cursor and Windsurf are IDE-based tools that integrate AI directly into your code editor. Vibe coding with Cursor gives you a chat panel and inline prompts that modify your actual files in real time. If you prefer a visual, file-tree-oriented workflow where you can see changes as they happen, these are the tools to start with. Cursor has the larger community and deeper feature set; Windsurf (by Codeium) offers a polished alternative with strong multi-file editing.

Vibe Coding with Claude Code and Gemini CLI

Vibe coding with Claude Code is a terminal-first experience. Anthropic's AI coding agent excels at complex, multi-file operations — the kind of work where you need AI to understand your entire project structure, not just the file you're looking at. It reads your codebase, plans changes across multiple files, runs tests, and commits code. For agentic workflows (where AI operates more autonomously), Claude Code is the most capable option available.

Gemini CLI is Google's entry, offering a generous free tier and one of the largest context windows. It's a strong choice for open-source projects and developers who want to experiment with vibe coding without upfront costs.

Pair Programming Tools: GitHub Copilot

GitHub Copilot is the most widely adopted AI coding tool, with millions of active users. It works as an inline suggestion engine — as you type, it predicts what you'll write next. While not a full vibe coding tool in the conversational sense, Copilot is often the entry point that introduces developers to AI-assisted coding. Many vibe coders use Copilot alongside a more capable tool like Claude Code or Cursor.

What Vibe Coding Gets Right

Speed to Prototype

The most obvious advantage: vibe coding is fast. Prototypes that took weeks can be built in hours. MVPs that took months can ship in days. This isn't hype — it's the consistent experience reported by developers across the industry. When you remove the bottleneck of translating ideas into syntax, the speed of development becomes limited by the speed of your thinking, not your typing.

Accessibility for Non-Developers

Vibe coding has opened software creation to people who couldn't build apps before. Designers who understand user flows but can't write React. Product managers who know exactly what the feature should do but never learned TypeScript. Domain experts — doctors, teachers, small business owners — who have ideas for tools that solve their specific problems. For the first time, you can build an app with AI, no coding experience required, and the result is real, deployable code you own.

Focused Creativity

Perhaps the most underrated benefit: vibe coding lets you stay in the creative zone. Instead of context-switching between "what should this feature do?" and "how do I implement this in code?", you stay focused on the product vision. The AI handles the implementation details, and you steer the direction.

Common Misconceptions About Vibe Coding

Vibe coding has grown so fast that myths have grown with it. Here are the most common ones:

"AI does everything — you just sit back." In reality, vibe coding is a collaboration. You define the direction, review the output, catch mistakes, and iterate. The developer's role shifts from writer to director, but the expertise still matters.
"Vibe-coded projects can't go to production." They absolutely can — with the right foundation. The projects that fail in production are usually the ones built ad-hoc from a blank canvas. Start with a proven architecture and quality checks, and vibe-coded code can be as reliable as hand-written code.
"You don't need to understand code at all." Some code literacy helps significantly. You don't need to write code from scratch, but being able to read what the AI produced, spot obvious issues, and understand error messages makes the process much more effective.
"Vibe coding is just a fad." Every generation of developer tools has abstracted away complexity. Compilers abstracted assembly. Frameworks abstracted HTTP. AI tools abstract implementation. Vibe coding is the next step in a decades-long trend, not a temporary phenomenon.
"All vibe-coded projects have the same quality problems." The quality issues come from unstructured AI usage, not from the methodology itself. When AI has architectural context and guardrails, the output quality is dramatically better than ad-hoc prompting.

Vibe Coding Best Practices for Beginners

If you're just getting started with vibe coding, these three practices will save you from the most common pitfalls.

Start with a Foundation, Not a Blank Canvas

The biggest mistake beginners make is asking AI to build everything from scratch. Research shows that AI-generated code has 70% more issues than human-written code when there's no architectural context to guide it (CodeRabbit, 2025). The AI doesn't know your project's conventions, patterns, or standards — so it invents new ones with every prompt.

The fix is straightforward: start with a proven codebase that gives AI the context it needs. Whether that's an existing project, an open-source boilerplate, or a commercial starter kit — the point is to give AI architectural context from the start. Add an AGENTS.md file that describes your conventions, set up linting, and establish patterns before you start prompting.

Use Structured Prompts, Not Ad-Hoc Requests

Instead of vague instructions ("build me a dashboard"), write structured descriptions that include the goal, the expected behavior, the data involved, and any constraints. The more context you provide upfront, the better the AI's first attempt — and the fewer iterations you'll need.

The most effective vibe coders use a PRD-driven workflow: they define features in a lightweight product requirements document before writing a single prompt.

Set Up Quality Gates Early

Don't wait until your project is large to add quality checks. Set up automated tests, type checking, and linting from day one. These quality gates catch AI-generated mistakes automatically — before they compound into larger problems. The difference between a vibe-coded prototype and a vibe-coded product is the automated verification layer that runs on every change.

When Vibe Coding Breaks Down (And How to Fix It)

Vibe coding isn't perfect, and it's important to understand where the limits are. The most well-documented challenge is pattern drift: when AI generates each feature using slightly different patterns, conventions, and approaches because it lacks memory of what it built before. Over time, this leads to code duplication (4x more than human-written code, per GitClear), inconsistent error handling, and security gaps (40%+ of AI-generated code contains vulnerabilities, per arXiv research).

The data on AI-generated code quality paints a clear picture: these are real problems — but they're solvable ones. The root cause isn't vibe coding itself; it's vibe coding without structure. When AI has access to your project's architectural context, enforced coding patterns, and automated quality checks, the output quality improves dramatically. The methodology works; it just needs guardrails. The emerging discipline of harness engineering formalizes how to build these guardrails systematically.

I hit this wall myself about three months into a project. Every feature worked in isolation, but the codebase had become a patchwork of inconsistent patterns. That's what pushed me to build tooling around the problem.

Getting Started with Vibe Coding Today

Ready to start vibe coding? Whether you want to build SaaS with AI or create your first side project, here's a practical path:

Pick an AI coding tool. Claude Code for terminal-first workflows, Cursor or Windsurf for IDE-integrated development, or GitHub Copilot for inline suggestions. Most developers end up using more than one.
Start with a proven foundation. Don't build from a blank canvas. Clone an existing project, use a boilerplate, or set up a well-structured repo with AGENTS.md and linting before you start prompting.
Follow a structured workflow. Plan features before prompting. Use structured descriptions. Run quality checks after every change.
Learn the practices that scale. Once you're comfortable with the basics, learn the patterns that separate prototypes from production applications.

Frequently Asked Questions

Is vibe coding the same as no-code?
No. No-code platforms like Bubble or Webflow use visual builders and limit you to their ecosystem. Vibe coding generates real source code — JavaScript, Python, TypeScript — that you own and can modify. You get an actual codebase, not a locked-in platform.

Can I build a production app with vibe coding?
Yes, but it requires structure. Unstructured vibe coding works great for prototypes but introduces consistency issues at scale. With a production-ready foundation, architectural context files, and quality gates, vibe-coded applications can absolutely run in production.

What's the best AI tool for vibe coding?
It depends on your workflow. Claude Code excels at multi-file agentic tasks from the terminal. Cursor and Windsurf offer the best IDE-integrated experience. GitHub Copilot is great for inline suggestions. Most developers combine two or more tools.

Do I need to know how to code to vibe code?
Some code literacy helps for reviewing AI output, but you don't need to be an expert. Many successful vibe coders are designers, product managers, or domain experts who understand what they want to build but couldn't write it from scratch.

How is vibe coding different from using GitHub Copilot?
Copilot suggests code completions as you type — it's a pair programmer. Vibe coding is broader: you describe entire features, review the output, and iterate through conversation. Copilot can be one tool in a vibe coding workflow, but the methodology encompasses the full build cycle.

What is structured vibe coding?
Structured vibe coding adds architectural context, quality gates, and repeatable workflows on top of the basic describe-and-generate loop. Instead of prompting AI ad-hoc, you give it context about your project's patterns, conventions, and standards — so it generates consistent, production-quality code every time. I wrote a deeper breakdown of structured vibe coding if you want the full framework.

I'm building VibeReady — an AI-native SaaS starter kit that gives AI tools the architectural context they need to generate consistent, production-quality code. If you're vibe coding and hitting the scaling issues described above, check it out.