Vibe Coding Workflow Guide: 4 Real Workflows You Can Use Today (2026)

Most vibe coding guides explain the concept. Few show you what to actually do on Monday morning.

You already know the idea — describe what you want, let AI generate code, iterate. But knowing the loop and having a repeatable workflow are different things. The difference between productive vibe coding and chaotic prompting is structure: knowing which tool to open, what to prompt first, when to review, and when to ship.

This guide covers four workflows for different situations. Each one includes the tool stack, actual prompts, and decision checkpoints. Pick the one closest to your scenario and adapt it.

For the conceptual foundations, see our guide on how vibe coding works. This article is all practice.

What Makes a Vibe Coding Workflow

A vibe coding workflow is a repeatable process for turning an idea into working software using AI tools. It's not "open Cursor and start typing prompts." It's a structured sequence with clear stages.

Every effective vibe coding workflow follows the same core loop:

Intent → Spec → Prompt → Generate → Review → Iterate → Ship

The difference between workflows is which tools handle each stage, how much review happens, and how fast you move through the loop. A solo developer prototyping a weekend project might blast through the loop in minutes. A team shipping production code might spend days on the spec and review stages.

What separates productive vibe coders from frustrated ones isn't the tool — it's having a process. As Andrew Ng put it: vibe coding "requires structuring your work, refining your prompts, and having a systematic process."

The 5 Stages of Every Vibe Coding Workflow

Before jumping into specific workflows, here's the universal framework. Every workflow in this guide maps to these five stages, just with different tools and time allocations.

Stage	What You Do	What AI Does	Key Prompt Pattern
1. Define	Write a spec: features, constraints, users, edge cases	Help brainstorm requirements, identify gaps	"I'm building X for Y users. Help me identify the core features and edge cases."
2. Scaffold	Choose tech stack, review project structure	Generate boilerplate, directory structure, config	"Set up a Next.js project with Supabase auth and Tailwind. Show the file structure first."
3. Build	Prompt feature-by-feature, review each output	Generate components, logic, API routes	"Add a dashboard page that shows user stats from the /api/stats endpoint."
4. Debug & Test	Paste errors, describe broken behavior	Diagnose issues, suggest fixes, write tests	"This error appears when I click submit: [paste error]. The form should save to the users table."
5. Ship	Configure deployment, review final output	Generate deploy configs, environment setup	"Create a Vercel deployment config for this project with these environment variables."

The time split varies. For a prototype, you might spend 10% on Define and 60% on Build. For a production feature, flip that — 40% on Define and Scaffold, 30% on Build, 30% on Debug & Test.

Workflow 1: Solo Developer Building a SaaS MVP

Best for: Developers who can read code, building MVPs and side projects.

Tool stack: Cursor (IDE) + Supabase (backend) + Vercel (deploy)

Estimated time: 4–8 hours from idea to deployed MVP

Stage 1: Define (30 min)

Start a new chat in Cursor Composer. Don't ask it to write code yet. Use the first conversation to nail down the spec.

I'm building a habit tracker SaaS. Target users are people who want
a simple, fast daily check-in — not a complex life management tool.

Core features:
- User auth (email + Google)
- Daily habit check-in (checkboxes)
- 7-day and 30-day streak tracking
- Simple dashboard with completion rate

Tech stack: Next.js 14, Supabase (auth + db), Tailwind CSS, Vercel deploy.

Before writing any code, outline the database schema, page structure,
and key API routes. I want to review the architecture first.

Review what comes back. Look for missing tables, unclear routes, or features that got over-engineered. Push back on anything that feels too complex for an MVP.

Stage 2: Scaffold (20 min)

Once the architecture looks right, ask Cursor to generate the project skeleton.

Create the project structure based on the architecture we discussed.
Start with:
1. Supabase schema (SQL for the habits and check_ins tables)
2. Next.js project with the page routes
3. Supabase client setup with environment variables
4. Auth configuration (email + Google OAuth)

Don't build features yet — just the scaffold.

Checkpoint: Run the project. Does it start? Can you see the login page? Does Supabase connect? Don't move forward until the scaffold runs clean.

Stage 3: Build (2–4 hours)

Build feature by feature. One prompt per feature. Review and test each before moving to the next.

Build the daily check-in page:
- Fetch the user's habits from Supabase
- Show each habit as a checkbox
- When checked, insert a record into check_ins with today's date
- Show a simple streak count next to each habit
- Use Tailwind for styling — keep it minimal

After each feature, commit your code. This isn't optional. If you can point to the exact commit that broke something, the AI can fix it much faster.

git add . && git commit -m "feat: daily check-in page with streak count"

Stage 4: Debug & Test (1–2 hours)

When something breaks (it will), paste the full error into Cursor:

When I check a habit, I get this error in the console:

[paste full error stack trace]

The check_in should be inserted into the check_ins table with the
user_id, habit_id, and today's date. The habit row should show
the updated streak count after checking.

Context helps. Don't just paste the error — describe what should happen. The AI fixes things faster when it knows the intent, not just the symptom.

Stage 5: Ship (30 min)

Create a Vercel deployment configuration for this project.
I need:
- Environment variables for NEXT_PUBLIC_SUPABASE_URL and
  NEXT_PUBLIC_SUPABASE_ANON_KEY
- A vercel.json if needed
- Instructions for connecting the GitHub repo to Vercel

Final checkpoint: Deploy to a preview URL. Test the full flow: sign up → create habits → check in → see streak. If it works, promote to production.

Cost for this workflow

Cursor Pro at $20/month, Supabase free tier, Vercel free tier. Total: $20/month to start.

Breakout: The Framework-First Workflow (Wasp + Cursor)

There's a variant of the solo developer workflow worth calling out separately: using an opinionated full-stack framework alongside your AI IDE.

Wasp is an open-source framework that lets you define your app's structure in a declarative .wasp config file — routes, auth, database models, server actions — and it generates the React frontend, Node.js backend, and Prisma ORM layer for you. When you pair this with Cursor or another AI IDE, something interesting happens: the AI isn't generating arbitrary full-stack code from scratch. It's working within a framework's conventions, which means fewer hallucinated patterns and more consistent output.

The workflow looks like this:

1. Define your app in the .wasp file. This is where you declare pages, routes, auth methods, and database entities. It's a short config — maybe 30–50 lines for an MVP.

app HabitTracker {
  wasp: { version: "^0.15.0" },
  title: "HabitTracker",
  auth: {
    userEntity: User,
    methods: { google: {}, email: {} }
  }
}

entity Habit {=psl
  id        Int      @id @default(autoincrement())
  name      String
  userId    Int
  user      User     @relation(fields: [userId], references: [id])
  checkIns  CheckIn[]
psl=}

route DashboardRoute { path: "/dashboard", to: DashboardPage }
page DashboardPage { component: import { Dashboard } from "@src/pages/Dashboard" }

2. Let AI handle the page components and server logic. Once the framework structure exists, prompt Cursor to build within it:

Using the Wasp entities defined in main.wasp, build the Dashboard
page component at src/pages/Dashboard.tsx. It should:
- Fetch all habits for the current user using a Wasp query
- Display each habit with a checkbox for today's check-in
- Show a 7-day streak count next to each habit
- Use the Wasp useQuery hook for data fetching

3. The framework catches mistakes the AI makes. If the AI generates a query that doesn't match your Wasp entities, the compiler tells you immediately. You're not hunting through a custom backend to figure out why something broke — the framework's type system flags it.

Why this matters for vibe coding: The biggest risk in Workflow 1 is that the AI generates a sprawling custom backend with inconsistent patterns. A framework like Wasp acts as a structural guardrail. The AI fills in the details, but the architecture stays clean because the framework enforces it. This is particularly useful if you're building something you plan to maintain beyond the prototype stage.

Cost: Wasp is free and open-source. Combined with Cursor Pro ($20/month), Supabase free tier, and a hosting provider, you're at the same $20/month as Workflow 1 but with a more maintainable output.

Workflow 2: Designer Shipping a Landing Page

Best for: Designers, founders, and non-developers who want a live site without writing code.

Tool stack: Figma (design) + Bolt.new (build) + Vercel or Netlify (deploy)

Estimated time: 1–3 hours from design to live site

This workflow is different. You're not writing code or reviewing code. You're describing visual outcomes and iterating on what you see in the browser.

Stage 1: Define (15 min)

Start in Bolt.new with a detailed description of what the page should look like and do.

Build a landing page for a productivity app called "FocusFlow."

Structure:
- Hero section: headline "Deep Work, Done Right", subhead about
  helping remote workers focus, CTA button "Start Free Trial"
- Features section: 3 cards (Pomodoro Timer, Focus Score, Team Insights)
- Social proof section: 3 testimonial cards
- Pricing section: Free and Pro tiers in a comparison table
- Footer with links

Style: clean, modern, lots of white space. Primary color: #2563EB.
Use Inter font. Mobile-responsive.

Stage 2 & 3: Build iteratively (1–2 hours)

Bolt.new generates the full page. Now iterate visually. Look at the preview and describe changes:

The hero section needs more vertical padding — double it.
Move the CTA button to the left, aligned with the headline.
Make the feature cards equal height with icons above each title.

Each prompt should address one visual concern. Trying to change five things at once leads to regressions.

Pro tip from Tal Raviv's experience with this workflow: "All I was thinking about was what I wanted to try, and how the result felt." That's the right mindset. You're not debugging code — you're iterating on feel.

Stage 4: Polish and test

Check the page on mobile. Bolt.new renders a preview, but test on an actual device or browser devtools.

The pricing table breaks on mobile — the columns stack but the
text overflows. Make the table responsive: single column on mobile
with each plan as a card.

Stage 5: Deploy

Bolt.new can deploy directly, or you can export to GitHub and deploy via Vercel. For a landing page, Bolt's built-in deploy is usually fine.

Cost for this workflow

Bolt.new free tier gives you 150K tokens/day — enough for a single landing page. Pro at $20/month for ongoing iteration.

Workflow 3: Team Workflow with AI Code Review

Best for: Teams of 2+ developers shipping production features.

Tool stack: Cursor or Windsurf (individual development) + Claude Code or GitHub Copilot (code review) + GitHub (PRs and CI)

Estimated time: Varies by feature scope

Team vibe coding is where most guides fall short. It's not just "everyone uses Cursor." You need conventions around how AI tools interact with your codebase and review process.

The team workflow pattern

Role	Tool	Responsibility
Developer A	Cursor / Windsurf	Feature development with AI assistance
Developer B	Cursor / Windsurf	Parallel feature development
AI Reviewer	Claude Code / Copilot	PR review, consistency checks
CI Pipeline	GitHub Actions	Automated tests, linting, type checks

How it works in practice

1. Spec together, build separately. The team writes the feature spec in a shared doc. Each developer takes a piece and builds it with their AI IDE.

2. Branch strategy stays the same. Feature branches, PRs, code review. The AI doesn't change your Git workflow — it changes how fast code appears on each branch.

3. AI-assisted code review. When a PR opens, use Claude Code or GitHub Copilot to do a first-pass review:

Review this PR for:
- Security issues (SQL injection, XSS, auth bypasses)
- Consistency with our existing patterns in /src/components
- Missing error handling for API calls
- Test coverage gaps

Our stack: Next.js, TypeScript strict mode, Supabase.

4. Human review is still required. The AI catches patterns and obvious issues. Humans catch architectural problems, business logic errors, and whether the code actually does what the ticket asked for.

Simon Willison draws a clear line here: "I won't commit any code to my repository if I couldn't explain exactly what it does to somebody else." In a team context, that means every PR gets reviewed by a human who can explain it — not just approved because the AI said it looked fine.

When team vibe coding works

It works when the team has strong conventions — shared linting rules, TypeScript strict mode, automated tests in CI. AI tools thrive when guardrails exist. Without guardrails, you get inconsistent code from different AI models and it compounds fast.

Cost for this workflow

Cursor Teams at $40/user/month, or individual Cursor Pro ($20/mo) + GitHub Copilot Business ($19/user/mo). Budget $40–60/developer/month.

Workflow 4: Debugging and Recovery

Best for: Anyone whose vibe-coded project just broke.

Every vibe coding session hits the wall eventually. The AI introduces a bug. A change breaks something unrelated. The fix for one error creates two more. This workflow is about getting unstuck.

The error-paste workflow

When something breaks, follow this sequence:

Step 1: Copy the full error. Not a summary. The entire stack trace, console output, or error message.

Step 2: Give the AI context about what should happen.

I see this error when clicking the "Save" button on the settings page:

[full error]

Expected behavior: clicking Save should update the user's profile
in the Supabase users table and show a success toast.

Current behavior: the page crashes with the error above.

Relevant files: src/pages/settings.tsx, src/lib/supabase.ts

Step 3: Review the fix before accepting. When the AI suggests a fix, read the diff. Does the fix address the root cause, or does it work around it?

When to reset vs. when to iterate

This is the hardest judgment call in vibe coding. Here's a rough guide:

• Iterate when the bug is localized (one file, one function, clear error)
• Reset to last working commit when the AI has made 3+ attempts at the same bug and it's getting worse
• Switch to manual coding when you understand the problem but the AI keeps suggesting the wrong approach

# Reset to last known good state
git log --oneline -10        # Find the last working commit
git stash                    # Save current changes just in case
git checkout <commit-hash>   # Go back to working state

The complexity ceiling

At some point in larger projects, AI-generated fixes start creating more problems than they solve. This is the "complexity ceiling" — the point where the AI's context window can't hold enough of your codebase to make safe changes.

When you hit it: break the problem into smaller pieces. Instead of "fix the dashboard," ask "what's causing the TypeError on line 47 of dashboard.tsx?" Narrow scope gives the AI better results.

For more on working with AI at this stage, see our prompt engineering guide.

Choosing the Right Workflow for Your Project

Not every project needs the same workflow. Here's a decision framework:

Project Type	Recommended Workflow	Best Tool Stack	Complexity Level
Landing page / marketing site	Workflow 2 (Designer)	Bolt.new or Lovable	Low
Weekend side project	Workflow 1 (Solo Dev)	Cursor + Supabase	Low–Medium
SaaS MVP for fundraising	Workflow 1 or Framework-First	Cursor + Supabase + Vercel (or Wasp)	Medium
Full-stack app you'll maintain	Framework-First (Breakout)	Wasp + Cursor + Supabase	Medium
Internal tool for your team	Workflow 1 or 3	Replit or Cursor + Supabase	Medium
Production feature (team)	Workflow 3 (Team)	Cursor/Windsurf + GitHub + CI	High
Fixing broken vibe-coded app	Workflow 4 (Debug)	Same stack + Git history	Varies

Budget considerations: Free tiers exist for most tools. A solo developer can vibe code for $20/month (Cursor Pro). Teams should budget $40–60 per developer per month. See our ecosystem guide for detailed tool comparisons.

Workflow Best Practices That Actually Matter

These are the practices that actually change outcomes, not generic advice:

Write a spec before you prompt. Even two paragraphs describing what you're building, who it's for, and what the core features are. Feed the spec to the AI. This single habit eliminates most of the "the AI built the wrong thing" complaints.

Commit after every working change. Not every change. Every working change. When something breaks, you can pinpoint exactly which commit introduced the bug, and the AI can fix it from there. Karpathy stressed this — capturing your commit history is critical for debugging.

Never accept code you can't explain. This is the line between vibe coding and vibe engineering. For prototypes and throwaway projects, "Accept All" is fine. For anything you'll maintain, you need to understand what the AI wrote. If you can't explain a function to a colleague, don't ship it.

Summarize context between prompts. AI tools lose context in long conversations. Every 5–10 prompts, summarize where you are: what's been built, what's left, and what the current problem is. This resets the AI's focus.

Set up linting and type checking early. AI tools perform dramatically better when your project has TypeScript strict mode, ESLint, and Prettier configured. The guardrails catch AI mistakes automatically.

Common Workflow Mistakes (and How to Fix Them)

One-shot generation. Trying to build the entire app in a single prompt. The AI will produce something that looks complete but has subtle bugs everywhere. Fix: build feature by feature.

Skipping the spec. Jumping straight into prompting without defining what you're building. You end up iterating on the wrong thing. Fix: spend 15–30 minutes on a spec, even a rough one.

Not using version control. Building in Bolt.new or Replit without connecting to Git. When something breaks, you can't go back. Fix: connect to GitHub from the start.

Ignoring the complexity ceiling. Continuing to pile AI-generated code into a growing codebase without understanding the architecture. Eventually every fix breaks something else. Fix: pause, read the code, understand the architecture, then continue.

Trusting AI output without review. Clicking "Accept All" on production code. The cautionary tales are real — from deleted databases to exposed user data. Fix: review every change that touches auth, data, or payments.

FAQ

What is a vibe coding workflow?

A vibe coding workflow is a structured process for building software with AI tools. You describe what you want in natural language, AI generates the code, and you iteratively refine through prompts until the software works. The key word is structured — effective vibe coding follows repeatable stages, not random prompting.

What tools do I need to start vibe coding?

At minimum: an AI-powered code editor like Cursor ($20/month) or a browser-based builder like Bolt.new (free tier). Add version control with Git. For backend work, pair with Supabase (free tier). For deployment, Vercel (free tier). You can start a full vibe coding workflow for $0–20/month.

Can I use vibe coding for production applications?

Yes, with guardrails. Production vibe coding needs code review, automated testing, and human oversight at every stage. Simon Willison calls this "vibe engineering" — using AI tools while maintaining full understanding and ownership of the code. The workflows in this guide include those checkpoints.

How long does it take to build an app with vibe coding?

A landing page: 1–3 hours. A functional MVP with auth and database: 4–8 hours. A production-ready feature: days to weeks, depending on complexity. The speed gain over traditional development is largest for prototypes and MVPs.

Is vibe coding faster than traditional coding?

For prototyping, clearly yes. For production work with experienced developers, it's more nuanced. A METR study found experienced open-source developers were actually 19% slower on their own familiar codebases when using AI tools. The speed benefit depends on project type, your experience level, and how well-structured your workflow is.

What's the difference between vibe coding and AI-assisted coding?

Vibe coding means building with AI without deeply reviewing every line — best for prototypes and low-stakes projects. AI-assisted coding means using AI as a productivity tool while maintaining full code ownership. Most working developers operate somewhere on the spectrum between the two.

Do I need to know how to code to vibe code?

Not for simple projects (landing pages, basic tools). But coding knowledge becomes critical as complexity grows — you'll need to debug issues, spot security vulnerabilities, and understand architecture decisions. The non-developer workflow (Workflow 2 above) is designed for people without coding experience.

What are the biggest risks of vibe coding?

Security vulnerabilities in unreviewed code, technical debt from AI-generated output that nobody understands, and the complexity ceiling where AI tools start creating more bugs than they fix. All three are manageable with the right workflow — which is why having one matters.

Can teams use vibe coding workflows?

Yes — Workflow 3 above covers this. Team vibe coding works best when you combine AI code generation with traditional engineering practices: PRs, code review, CI/CD, and shared conventions. The AI accelerates individual developer output; the team process ensures quality.

How do I debug vibe-coded applications?

Paste the full error (not a summary) into your AI tool. Include context about expected vs. actual behavior. Point to the specific files involved. If the AI can't fix it in 2–3 attempts, reset to the last working commit and try a different approach. See Workflow 4 above for the detailed process.