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

You’ve heard about vibe coding. You know AI can write code. But you haven’t actually sat down and done it yet — because it’s hard to know where to start.

This is a practical, step-by-step vibe coding tutorial. By the end, you’ll know how to vibe code a real feature for a SaaS application — not a toy demo, but something you can actually ship. No specific tool required, no specific framework assumed. New to the concept? Read What Is Vibe Coding? first for background.

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.

1. 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.
2. 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.
3. 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.
4. 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. Compare AI coding tools →
5. 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. See starter options →

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:

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

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.

For the full strategic approach to planning a SaaS with AI tools, see our guide to building SaaS with AI.

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.

See our honest assessments of Lovable and Bolt to understand what you’re gaining and giving up. Not sure which path to take? Read our decision framework →

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. Compare popular options or see VibeReady editions from $149 →
• 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. See vibe coding best practices →

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/”)

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.

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

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:

1. Logic check. Does the feature actually do what the spec says? Test the happy path and at least one edge case.
2. Security basics. Are inputs validated? Are database queries parameterized? Are auth checks in place?
3. Pattern consistency. Does the code follow the same patterns as the rest of your project? Or did AI invent a new approach?
4. 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.

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 →

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:

1. 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.
2. 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.
3. 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.

Vibe Coding Tutorial: Frequently Asked Questions

Have more questions? See our full FAQ →