AI Coding Workflows: How the Best Vibe Coders Actually Work

Most vibe coders approach AI like a fancy autocomplete. They ask one question, accept the first answer, move on. Then they’re confused when the code doesn’t work or doesn’t fit their project.

The best vibe coders treat AI like a collaborative partner. They have systems.

A system for how to ask questions. A system for managing context. A system for iterating when something’s wrong. A system for knowing when to start fresh. A system for reviewing AI output before shipping it.

This isn’t magic. It’s workflow.

This guide covers the exact systems that separate vibe coders who ship fast from vibe coders who ship fast and reliably. You’ll learn the iterative loop, how to structure prompts across a session, when to start new conversations, how to manage context windows, how to orchestrate multi-file changes, debugging patterns that actually work, code review loops, and testing workflows.

The Core Loop: Describe → Build → Review → Iterate

Every successful AI coding session follows this pattern:

1. Describe (clearly and specifically)
2. Build (AI generates code)
3. Review (you check for errors and fit)
4. Iterate (ask for changes based on what you found)
5. Test (does it actually work?)
6. Repeat until done

The vibe coder’s edge is that you’re comfortable with iteration. You don’t expect the first attempt to be perfect. You expect it to be 80% right and then refine it five times.

Example: Building a Payment Form

Prompt 1: Describe the shape

I'm building a payment checkout flow in Next.js with TypeScript. I need a component called PaymentForm that:
- Takes a `onSuccess` callback prop
- Renders an input for card number (only numbers, max 16 chars)
- Renders expiry month/year dropdowns
- Renders CVV input (numbers only, 3-4 chars)
- Shows validation errors below each field in red text
- Has a "Pay" button that's disabled while processing
- Has a loading state that shows "Processing..." on the button

Don't write the actual Stripe integration yet. Just the form UI and basic validation. I'll handle the backend separately.

AI gives you a basic React component. It’s not perfect, but it has the structure.

Review: You read the code. Three problems: The expiry year dropdown goes to 2099 (should be 2050). The button text says “Subscribe” not “Pay”. The validation errors render even when the field is untouched.

Prompt 2: Refine the validation

I see three issues:

1. The expiry year dropdown shows 2099. Change it to current year + 20 years.
2. The button text is "Subscribe" but should be "Pay".
3. The validation errors show even for untouched fields. Only show errors if the field has been touched AND has a value AND the value is invalid.

Also add a boolean state to track which fields have been touched. Here's the pattern I want:
- Field is touched: user clicked into it and clicked out
- Field is invalid: user entered something and it doesn't match the pattern
- Show error: touched AND invalid

Apply these fixes to the component.

AI refines the component. Much closer.

Review: Better. But the form doesn’t actually prevent submission if fields are invalid. The button should be disabled if any field fails validation.

Prompt 3: Add validation state

The form still lets you submit if fields are invalid. I need:
- Compute a `isValid` boolean: true only if all required fields are filled and match their patterns
- Disable the "Pay" button if `isValid` is false
- Also disable the button if `isProcessing` is true

Show me the updated component.

AI updates the validation logic. Now it actually prevents invalid submissions.

Test: You paste the component into your Next.js app. You test the UI: typing, validation errors appearing correctly, button disabling when fields are invalid. It works.

Done. You’ve shipped a payment form component in 3 iterations instead of getting bogged down in the first attempt.

Notice what happened: You didn’t ask for the whole solution at once. You asked for the structure, reviewed it, then refined it iteratively. Each iteration was small and focused.

This is the vibe coder advantage.

Structuring Prompts Across a Session: The Context Pyramid

Every conversation with AI has a context window—a limit on how much information AI can see at once. For Claude, it’s 200k tokens. For GPT-4, it’s 128k or 200k.

You can’t load your entire codebase into every prompt. You have to be strategic.

The Context Pyramid: Start Broad, Narrow Down

Bottom level (broad context): Your project’s architecture, tech stack, coding standards, conventions.

Middle level (specific context): The files relevant to this feature.

Top level (laser focus): The exact problem you’re solving right now.

Example: Building a User Authentication Flow

Session start: Establish context (Prompt 1)

I'm building a SaaS auth system in Next.js 15 with TypeScript. Here's the architecture:

Stack:
- Next.js 15 with App Router
- React Server Components where possible
- Supabase for auth (using nextjs-supabase-auth)
- Tailwind CSS + shadcn/ui for components
- PostgreSQL database via Supabase
- TypeScript strict mode, no `any` types

File structure:
- /app → pages and layouts
- /components → reusable UI components
- /lib → utilities, API helpers, auth helpers
- /types → TypeScript types

Coding standards:
- Keep components under 150 lines
- Use custom hooks for logic
- Error messages should be user-friendly, never expose stack traces
- API responses follow: { success: boolean, data?: T, error?: string }

I'm building an auth flow: signup → verify email → dashboard. We'll build this in pieces.

AI now has your project’s DNA. Every subsequent prompt can reference this context without you repeating it.

Middle level: Feature specifics (Prompt 2)

Let's start with the signup page. It should:
- Live at /app/auth/signup/page.tsx
- Have a form with email + password inputs
- Password must be at least 8 chars
- Show validation errors below each field
- Submit to /api/auth/signup (we'll create this endpoint next)
- On success: show a message "Check your email to verify" and disable the form
- On error: show the error message in red

Build just the page. Don't worry about the API yet.

AI builds the signup page component. You review it, ask for tweaks, iterate.

Top level: Laser focus (Prompt 3)

The password field should also check:
- At least one uppercase letter
- At least one number
- Not contain the user's email address

Update the validation function to check all three. Show me just the updated validation function and the error messages that go with it.

AI gives you the validation logic. You’re moving fast because the context is tight.

Why This Works

Each level builds on the previous one. You’re not re-explaining your entire project in every prompt. You’re re-using context. You also aren’t overwhelming the AI with irrelevant code.

Key principle: The more specific the prompt, the more accurate the response. But you can only get specific after establishing the broader context first.

Managing Context Windows: When to Know You’re Running Out of Space

Claude’s 200k token context is huge. You can fit an entire medium-sized codebase in one conversation.

But you’re not unlimited. Eventually, you hit a wall.

Signs You’re Running Out of Space

1. AI starts forgetting previous constraints. You said “use Tailwind for styling” at the beginning and now it’s using inline styles.

2. Responses get slower. The model is processing more tokens, so latency increases.

3. Quality drops. The AI starts making mistakes it didn’t make earlier in the conversation.

4. You paste a code block and AI says it can’t see it. Not common, but if it happens, you’ve definitely run out of context.

How to Manage Context

Option 1: Start a new conversation

When you notice quality dropping, start fresh. Copy your architecture notes and paste them in the new conversation. You’ve now reset the context window and can continue with a clean slate.

Option 2: Archive context explicitly

Before starting a new conversation, have AI write a summary of what you’ve built so far:

Let's take stock of what we've built so far. Can you write a summary of:
- The files we created
- What each file does
- What still needs to be done

Keep it brief.

Paste that summary into your next conversation as context. You’ve preserved what matters without keeping the entire 100-message thread.

Option 3: Delete irrelevant messages

Some platforms let you delete messages from a conversation. If you have a 200-message thread, deleting the first 100 messages (which are now archived) clears context without losing what you need.

Practical Threshold

For most vibe coders: Start a new conversation after 30-50 messages or when you finish a major feature.

New conversation = fresh start = better quality responses. It’s not a failure. It’s a system.

Multi-File Orchestration: Building Features Across Multiple Files

Real projects don’t live in one file. A payment feature touches your component, your API, your database schema, your types.

The best vibe coders coordinate AI changes across multiple files.

Pattern: The Feature Breakdown

Before you start, list the files that need changes:

Building a “favorites” feature:

Files that need changes:

• /types/db.ts — Add favorite schema
• /lib/db.ts — Add query to save/remove favorites
• /components/favorite-button.tsx — New button component
• /app/api/favorites/route.ts — API endpoint
• /app/favorites/page.tsx — Favorites dashboard page

Prompt 1: The Schema

I want to add a favorites feature. Users can mark items as favorites and see a list of all their favorites.

Database schema for a favorite:
- id (uuid, primary key)
- user_id (uuid, foreign key to users table)
- item_id (uuid, foreign key to items table)
- created_at (timestamp)

Add this table to my Supabase schema. Also write the TypeScript type for a Favorite. Create both in @types/db.ts.

AI creates the schema and type. Done.

Prompt 2: The Query Helper

Now I need helper functions to:
- `toggleFavorite(userId, itemId)` — If favorite exists, delete it. If not, create it. Return whether it's now favorited.
- `getFavorites(userId)` — Return all items the user has favorited.

Write these in @lib/db.ts. Use the Supabase client that's already imported there.

AI writes the query helpers. Done.

Prompt 3: The Component

Create a button component @components/favorite-button.tsx that:
- Takes props: itemId, isFavorited (boolean), onToggle (callback)
- Shows a heart icon (use lucide-react)
- Filled heart if isFavorited, outline if not
- On click: call onToggle, then toggle the icon state
- Show a loading spinner while the toggle is in progress
- Use these styles: lucide icons, Tailwind utilities (no inline styles)

AI builds the component. Done.

Prompt 4: The API

Create an API endpoint @app/api/favorites/route.ts that:
- POST /api/favorites with body: { itemId }
- Uses the `toggleFavorite` helper from @lib/db.ts
- Returns { success: boolean, isFavorited: boolean }
- Returns 401 if the user isn't authenticated

AI creates the endpoint. Done.

Prompt 5: The Page

Create @app/favorites/page.tsx that:
- Fetches the user's favorites using `getFavorites`
- Displays them in a 2-column grid
- Uses the FavoriteButton component to let users un-favorite
- Shows "No favorites yet" if the list is empty

AI builds the page. Done.

Test everything together: The full feature works end-to-end.

Notice the progression: Schema → Query helpers → Component → API → Page. Each piece builds on the previous one. You’re not asking for the whole feature at once. You’re building it in layers, reviewing each piece, then moving to the next.

This is more reliable than asking for the entire feature in one giant prompt.

Debugging with AI: When Code Doesn’t Work

You wrote AI-generated code. It doesn’t work. How do you debug?

Pattern 1: The Error Message is Your Guide

Your code crashes with: Cannot read property 'email' of undefined

Effective prompt:

I'm getting this error:

Cannot read property ‘email’ of undefined at getUserEmail (lib/auth.ts:42)

Here's the relevant code:
@lib/auth.ts
@components/profile.tsx

What's causing this? How do I fix it?

Paste the error, paste the relevant files, ask what’s wrong. AI can usually spot it immediately.

Pattern 2: Behavioral Bugs (Code Runs But Does Wrong Thing)

Problem: The “delete item” button deletes all items instead of just one.

Effective prompt:

The delete button is removing all items when I click it, not just the clicked item. Here's the relevant code:

@components/item-list.tsx
@app/api/items/[id]/route.ts

I click the delete button on one item and all items disappear. Where's the bug?

AI reads the code, spots that you’re not passing the item ID to the delete API, and tells you to fix it.

Pattern 3: Understanding Unexpected Behavior

Problem: Something happens but you’re not sure why.

I notice that when I submit the form, the API returns 200 but the frontend doesn't update. I check the network tab and the response has `{ success: true, data: {...} }` but my UI doesn't change.

Here's my submit handler:
[paste code]

And here's my state:
[paste code]

Why isn't the UI updating?

AI points out that you’re not calling setData() with the response, and that’s why the UI doesn’t update.

The Debugging Workflow

1. Reproduce the bug locally. Make sure it’s consistent.
2. Get the exact error message (from console, network tab, terminal).
3. Paste the relevant code (use @ mentions if you’re in Cursor).
4. Ask specifically (“why is X happening?” not “why is my code broken?”).
5. Try the fix AI suggests.
6. If it doesn’t work, show AI the fix didn’t help and iterate.

Most bugs are caught on the first try with this approach.

Code Review Loop: AI as Your Second Brain

Never ship code without reviewing it first. This applies whether the code came from AI or you wrote it yourself.

But how do you review AI code when you’re not sure what it’s supposed to do?

Pattern: Explain-Then-Review

Before you accept AI code, have AI explain it:

Here's the code you just generated for the authentication middleware:

@lib/auth-middleware.ts

Walk me through what this code does, line by line. Specifically:
- What happens on line 15?
- Why are you checking `user?.role` before creating the session?
- What's the purpose of the `setMaxAge` option?

Then tell me if there are any security concerns I should know about.

AI explains the code. You understand what it does. You can now review it with confidence.

You might spot issues in the explanation:

• “Oh, I didn’t realize you were checking role-based access here. That’s good.”
• “Wait, you’re checking user?.role === 'admin' but we also have moderators. Should those have access too?”

Now you can ask for refinements based on understanding.

Security Review Pattern

For anything touching authentication, database, or sensitive data:

Review this code for security issues:

@lib/database.ts
@app/api/user/route.ts

Specifically check for:
- SQL injection (are queries parameterized?)
- Authentication bypass (is the user check correct?)
- Data leaks (are we exposing sensitive data in the response?)
- Validation (are we validating all inputs?)

AI will spot issues you might miss.

Testing Workflows: How to Test AI-Generated Code

AI can write tests as well as it can write code. Use it.

Pattern 1: Unit Test Generation

Write tests for this function using Jest. Test happy path and all error cases:

@lib/email-validator.ts

The function should:
- Return true for valid emails
- Return false for invalid formats
- Handle edge cases (very long emails, emails with special chars, etc.)

AI generates comprehensive tests. You run them locally: npm test. Most pass immediately.

Pattern 2: Integration Test Scenarios

For features that touch multiple files:

Write an integration test scenario for the signup flow. User story:
1. User visits /auth/signup
2. Enters email and password
3. Clicks signup
4. API validates input
5. Creates user in database
6. Sends verification email
7. Shows "Check your email" message
8. User clicks link in email
9. Marks email as verified in database
10. Redirects to dashboard

Write a test that covers this entire flow. Mock the email sending.

AI writes an end-to-end test. You run it. It catches integration bugs before they reach production.

Pattern 3: Error Case Testing

My password reset feature works for the happy path. But I haven't tested edge cases. Generate tests for:
- User enters email that doesn't exist
- Reset link is expired
- User clicks reset link twice (first time works, second time should fail)
- User submits new password that doesn't meet requirements

Make these tests realistic (use actual user scenarios).

AI writes edge case tests. You run them, fix bugs, ship more reliably.

Session Management: When to Start a New Conversation

You don’t have to keep a conversation going forever. Sometimes a new conversation is better.

When to Start Fresh

1. Switching projects or domains

If you finish building Feature A and now you’re building Feature B (different files, different logic), start a new conversation. The context from Feature A will distract the AI.

2. Major architectural change

You’re rebuilding part of your codebase. Start fresh. Paste your new architecture as the first message. The AI can now focus on your new design without reference to the old one.

3. Persistent bugs you can’t shake

If you’ve asked the same question 5+ times and AI keeps missing the issue, context is probably polluted. Start fresh, paste the exact problem, and iterate from there.

4. When quality visibly drops

If AI’s responses were great for the first 30 messages and now they’re mediocre, you’re probably context-limited. Archive your notes, start fresh.

When to Keep Going

1. You’re building one coherent feature across multiple files. Stay in the same conversation.

2. You’re iterating on the same component/file. The context is fresh and relevant.

3. You’re debugging a single issue. Continuity helps.

4. You’re fewer than 50 messages in. Plenty of context left.

Real Workflow Example: Building a Notification System

Here’s what a real session looks like, condensed:

Session: Notification System (roughly 25 prompts, 2 hours)

Prompt 1: Architecture overview (stack, folder structure, standards)

Prompt 2: Database schema (notifications table, types)

Prompt 3: Query helpers (save notification, get user’s notifications, mark as read)

Prompt 4: API endpoint (POST /api/notifications)

Prompt 5: Component: NotificationBell (shows icon + count badge)

Prompt 6: Component: NotificationPanel (dropdown showing list)

Prompt 7: Hook: useNotifications (fetches and manages state)

Prompt 8: Real-time updates (poll every 30s or use WebSocket)

Prompt 9: Integration in layout (add NotificationBell to nav)

Test: Click bell, see notifications, mark as read, count updates.

Prompt 10: Bugs found during testing (detail the issue)

Prompt 11: Fix the bug

Test again.

Prompt 12: Add animations (bell bounces when new notification)

Prompt 13: Add notification sounds (optional, off by default)

Prompt 14: Write tests for the hook

Prompt 15: Write tests for the API

Run tests locally. All pass.

Review the code. Everything looks good.

Deploy.

Total time: ~3 hours for a production notification system. Iterative, tested, reviewed.

If you’d done this without AI, you’d be looking at 12-16 hours. If you’d done it by accepting AI’s first answer on every prompt, you’d ship faster but with bugs.

The vibe coder’s advantage is the middle ground: Use AI to move fast but stay intentional about reviewing and iterating.

The Meta Skill: Knowing When You’re Stuck

The best vibe coders know when they need help.

Signs you should ask AI for help:

• You’re staring at code trying to understand what’s wrong
• You’ve tried the same fix three times and it still doesn’t work
• You don’t know what to ask because the problem is unclear
• You’re manually coding something repetitive

Signs you should NOT ask AI:

• You’re just testing or trying something small
• You know exactly what you’re doing but haven’t done it yet
• You need to understand something deeply (sometimes manual learning is faster)

The difference is judgment. The best vibe coders know when AI saves time and when it wastes it.

For you: If you’ve been working on one bug for 20 minutes and you’re not sure what’s wrong, ask AI. Describe what you tried, paste the code, ask for help. You’ll save an hour.

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Related Reading

• Prompt Engineering Guide — Write better prompts from the start
• Cursor Tips and Tricks — Power user techniques in your editor
• How to Start Vibe Coding — Foundations if you’re new
• Vibe Coding with Claude — Using Claude specifically for your workflows