AI Coding Workflow Mastery: Debug, Refactor, Ship

Generating AI code is easy. Shipping it to production? That's where 90% of developers crash and burn.

You've seen the hype: "Build a full app in 10 minutes with AI!" And sure, you can generate something that looks like an app. But then you click a button. Nothing happens. Check the console. Red errors everywhere. Try on mobile. It's a disaster. Ask AI to fix it. Now you have different bugs.

Welcome to the real AI coding workflow.

Here's the uncomfortable truth: generating code is maybe 20% of the work. The other 80%? Debugging, iterating, refactoring, and preparing that code for production. The developers who actually ship AI-generated apps aren't the ones with the best prompts—they're the ones with the best workflows.

This guide is that workflow. Everything you need to go from "AI spit out something" to "this is actually live and working."

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Key Takeaways:

• The AI coding workflow has 4 phases: Generate → Debug → Refactor → Ship. Most people skip phases 3 and 4.
• Debugging AI code is fundamentally different from debugging human code—you need to leverage AI as a debugging partner, not just a code generator.
• The prompt iteration cycle (identify → isolate → fix → verify) solves 80% of AI code issues in under 10 minutes.
• Production-ready AI code requires accessibility, SEO, performance, and security checks—none of which AI handles automatically.
• A consistent Tailwind/styling workflow prevents the "Frankenstein UI" problem where every component looks different.

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Table of Contents

1. The AI Coding Workflow Overview
2. Debugging AI-Generated Code
3. The Prompt Iteration Cycle
4. Refactoring & Code Cleanup
5. Tailwind CSS Workflow
6. Accessibility & WCAG Compliance
7. SEO Optimization
8. Production Checklist
9. FAQ

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Try this prompt

Copy

BUILD: Developer sprint board with Debug, Refactor, and Ship columns FOR: Solo developers managing a focused coding sprint MUST: - Three-column Kanban board labeled Debug, Refactor, and Ship - Draggable task cards with title, priority badge, and linked file path field - Quick-add task form at the top of each column SHOULD: - Column completion percentage counter in each header - Priority filter bar (critical / normal / low) above the board CONTEXT: React + Tailwind, minimal dark board UI (#111827), priority colors red/yellow/green, smooth CSS drag transitions

⌘+Enterto launchLaunch in Fardino

The AI Coding Workflow Overview

Let me map out what actually happens when you build something with AI—not the marketing version, the real version.

The Four Phases

Phase 1: Generate You write a prompt. AI generates code. This is the part everyone talks about.

Phase 2: Debug The code breaks (it will). You identify what's wrong, feed errors back to AI, and iterate until it works. This is where most people get stuck.

Phase 3: Refactor It works, but it's a mess. Duplicate code. Inconsistent patterns. Magic numbers everywhere. You clean it up so future-you doesn't want to cry.

Phase 4: Ship Make it production-ready. Accessibility. SEO. Performance. Security. The stuff AI doesn't think about.

Most tutorials focus exclusively on Phase 1 and pretend the others don't exist. That's why so many AI-generated projects never make it to production.

Why This Workflow Matters

Here's the math: if you spend 10 minutes generating code that takes 2 hours to debug and fix, you haven't saved time—you've wasted it. But if you follow a systematic workflow, you can compress that 2-hour debug session into 15 minutes.

The developers shipping real products with AI aren't faster at prompting. They're faster at the entire cycle.

Phase	Time (Amateur)	Time (With Workflow)	Key Difference
Generate	5 min	5 min	Same
Debug	2 hours	15 min	Systematic approach
Refactor	"Skip it"	20 min	Intentional cleanup
Ship	30 min of panic	30 min structured	Checklist-driven

Let's break down each phase.

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Debugging AI-Generated Code

AI-generated code breaks differently than human code. When you write code, you understand every line. When AI writes code, neither of you fully understands it—including the AI that wrote it.

This changes how you debug.

The Fundamental Problem

When your code breaks, your instinct is to ask AI: "Fix this." But here's the thing—AI doesn't remember what it wrote or why. Every prompt is a fresh start. It's like asking a stranger to fix code they've never seen.

The solution? Don't ask AI to fix. Ask it to explain first.

This is the core insight from our vibe debugging workflow: explanation before action.

The 5-Step Debugging Workflow

I use this workflow for every AI code bug. It works for trivial issues and nightmarish edge cases alike.

Step 1: Identify the Symptom (Not Your Assumption)

Bad: "The form validation is broken." Good: "When I click Submit with empty fields, no error messages appear. Console shows no errors."

Observable facts, not interpretations. AI can't fix "broken"—it needs specifics.

Step 2: Isolate the Component

Don't debug a 500-line file. Extract the suspected broken piece into a minimal component. If it still fails in isolation, the bug is in that code. If it works in isolation, the bug is in how it integrates with everything else.

Step 3: Ask AI to Explain

This is the step that changes everything. Before asking for a fix:

Half the time, the explanation reveals the bug immediately. "The handleSubmit function is async but there's no await for validation, so it returns before validation completes."

Step 4: Prompt for Targeted Fixes

Now—and only now—ask for a fix. But be surgical:

The magic words: "fix ONLY this issue" and "show me the corrected [specific thing] only."

Step 5: Document

Future-you will thank present-you:

For more detailed debugging strategies, including prompts you can copy directly, see our complete vibe debugging guide.

The Top 5 AI Code Errors (And How to Spot Them)

After debugging hundreds of AI components, these are the errors you'll hit constantly:

Error Type	What You'll See	The Actual Cause	Quick Fix
Import Errors	Module not found	AI assumed a package was installed	Install it or ask for native alternative
Type Errors	undefined is not an object	AI used properties that don't exist	Add null checks or fix data structure
State Bugs	Component doesn't re-render	Missing state updates or wrong deps	Check useState/useEffect logic
Mobile Breaks	Looks fine desktop, chaos on phone	No responsive classes	Add mobile-first Tailwind prefixes
Silent Event Handlers	Clicks do nothing	onClick={fn()} instead of onClick={fn}	Remove the parentheses

For deeper dives on each error type and copy-paste fix prompts, check our guide to fixing AI code errors.

The Error-Forward Technique

This is my bread-and-butter debugging method. It sounds almost too simple, but it fixes 80% of bugs:

1. Copy the exact error message from console
2. Copy the relevant code
3. Send both to AI: "This code produces this error. Fix it and explain what was wrong."
4. Run the fix
5. Repeat if needed

The key is giving AI complete context. Don't just paste the error—include the full component, browser console output, and what you were trying to do.

When to Regenerate vs. Debug

Sometimes fixing is faster. Sometimes starting fresh is smarter. Here's my decision framework:

Situation	Action
1-2 simple fixes worked	Keep debugging
Same bug keeps returning	Regenerate
Bug is in AI's architecture	Regenerate
Bug is in specific function	Debug
3+ failed fix attempts	Regenerate

My rule: if three targeted fix attempts don't solve it, regenerate with a better prompt that includes constraints learned from debugging.

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The Prompt Iteration Cycle

Debugging fixes broken code. But what if the code works, just not the way you want? What if the button is blue but should be green? The layout is wrong? The animation is janky?

That's where prompt iteration comes in.

The Iteration Mindset

Every AI output is a draft. Expecting perfection on the first try is setting yourself up for frustration. The prompt iteration workflow treats AI generation as a conversation, not a one-shot magic spell.

The 5-Step Iteration Cycle

Step 1: Generate Start with a clear, specific prompt. Include constraints upfront.

Step 2: Evaluate Don't just look at it—interact with it. Click buttons. Resize the window. Try edge cases.

Step 3: Identify the Gap What specifically is wrong? "It doesn't look right" won't help AI. "The button padding is too small and the text color doesn't contrast enough" will.

Step 4: Refine the Prompt Add constraints that address the gap:

Step 5: Regenerate Run the refined prompt. Evaluate again. Repeat until satisfied.

Iteration Anti-Patterns

Avoid these common mistakes:

Anti-Pattern 1: Starting Over Each Time Each prompt builds on the last. Don't rewrite from scratch—refine.

Anti-Pattern 2: Vague Feedback "Make it better" is useless. "Increase padding, darken the background, add hover states" is actionable.

Anti-Pattern 3: Too Many Changes at Once Fix one category of issue per iteration. Colors, then spacing, then interactions. Trying to fix everything at once leads to whack-a-mole.

Anti-Pattern 4: Infinite Iteration Set a limit. If 5 iterations don't get you there, the prompt itself is wrong. Reframe the problem.

For a complete breakdown of this workflow with templates you can use immediately, see our 5-step guide to fixing any AI UI.

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Refactoring & Code Cleanup

Here's the dirty secret of vibe coding: half the work is cleanup.

AI generates code that works. But "works" doesn't mean "maintainable." You'll find:

• Functions that do 10 things
• Hardcoded values everywhere
• Copy-pasted logic instead of shared functions
• Inconsistent naming
• No comments explaining why

If you skip refactoring, you're building technical debt at 10x speed.

The Refactoring Philosophy

Refactoring AI code isn't about making it "pretty." It's about making it changeable. Can you modify this component in six months without breaking everything? If no, refactor.

Refactoring Prompts That Work

You can use AI to refactor AI code. Here are the prompts that actually produce clean results:

The DRY Prompt (Don't Repeat Yourself)

The Extract Component Prompt

The Magic Numbers Prompt

The Naming Cleanup Prompt

For 20+ more refactoring prompts organized by use case, see our vibe refactoring guide.

The Refactoring Checklist

Before considering a component "clean," verify:

• No function exceeds 20 lines
• No component file exceeds 200 lines
• No repeated code blocks (DRY)
• All hardcoded values extracted to constants
• All props have TypeScript types
• Naming is consistent and descriptive
• Complex logic has comments explaining why
• Side effects are isolated in useEffect hooks
• Error states are handled explicitly

When NOT to Refactor

Refactoring isn't always worth it:

• Throwaway prototypes: If it's getting deleted next week, ship it messy.
• Working production code: Don't refactor things that work unless you're actively modifying them.
• Early iteration: Refactor after the design stabilizes, not during exploration.

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Try this prompt

Copy

BUILD: Runtime error log viewer with inline bug report form FOR: Developers triaging production bugs in a web application MUST: - Filterable error table with columns for type, message, frequency, and timestamp - Expandable stack trace panel that slides open below each selected row - Bug report form pre-filled from selected error (title, severity, steps to reproduce) SHOULD: - Status filter tabs (open / in-progress / resolved) - Copy full error as JSON button on each expanded row CONTEXT: React + Tailwind, dark two-pane layout, red error row highlights, monospace stack trace font, neutral sidebar

⌘+Enterto launchLaunch in Fardino

Tailwind CSS Workflow

If you're using AI for frontend development, you're probably using Tailwind. And if you're using Tailwind with AI, you've probably experienced the "Frankenstein UI" problem.

AI generates a button with bg-blue-500. Then a card with bg-indigo-600. Then a badge with bg-sky-400. You now have three different "primary" blues.

The fix isn't better prompts—it's a system.

The Design Token Foundation

Before generating any components, define your design tokens:

Include this in every component prompt. Copy-paste it. Make it a snippet. The AI can't go rogue if you've eliminated its choices.

The Component Generation Workflow

Follow this order for consistent UI systems:

Base Primitives First:

1. Button (primary, secondary, outline, ghost variants)
2. Input (text, email, password, error states)
3. Card (container with header/body/footer)
4. Badge (success, warning, error, info)
5. Avatar (sm, md, lg sizes)

Then Variations:

• Pricing card (uses Card + Badge + Button)
• User profile card (uses Card + Avatar + Button)
• Alert (uses Card styling + Badge colors)

Finally Layouts:

• Full pages composed of your established components

This is exactly the workflow detailed in our Tailwind component generation guide.

Component Prompt Template

Use this template for every Tailwind component:

Common Tailwind AI Mistakes

Mistake	Result	Prevention
No color constraints	Rainbow UI	Define palette in every prompt
Generated full page first	Inconsistent components	Build primitives first
Random spacing values	Visual chaos	Lock to spacing scale
Forgot responsive	Broken on mobile	Always specify mobile behavior
Mixed naming conventions	Confusion	Standardize on one system

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Accessibility & WCAG Compliance

Here's a stat that should scare you: 96% of home pages have detectable WCAG failures. AI-generated code is no exception—in fact, it's often worse because AI optimizes for visual appearance, not accessibility.

Shipping inaccessible code isn't just bad ethics. It's legal liability. And it's user abandonment—15% of the global population has a disability.

The Accessibility Fundamentals

At minimum, every AI component needs:

1. Semantic HTML AI loves <div> soup. Fix it:

• <button> for clickable actions (not <div onClick>)
• <nav> for navigation (not <div className="nav">)
• <main>, <header>, <footer> for page structure
• <h1> through <h6> in order (no skipping levels)

2. Color Contrast Text must be readable. WCAG requires:

• 4.5:1 contrast ratio for normal text
• 3:1 for large text (18px+ or 14px bold)

Use this prompt addition:

Ensure all text has a minimum 4.5:1 contrast ratio against its background.

3. Keyboard Navigation Everything clickable must be keyboard-accessible:

• Tab should move through interactive elements logically
• Enter/Space should activate buttons and links
• Escape should close modals

4. ARIA Labels When semantic HTML isn't enough:

Accessibility Audit Prompt

After generating any component, run this prompt:

The Accessibility Checklist

Before any component ships:

• All interactive elements are focusable
• Focus order is logical (tab through it)
• Color is not the only way to convey information
• Images have alt text (or aria-hidden if decorative)
• Form inputs have associated labels
• Error messages are announced to screen readers
• Modals trap focus and return it when closed
• Contrast ratios meet minimums

For a deep dive into accessibility prompts, including templates for specific component types, see our WCAG accessibility guide.

Making Existing AI Code Accessible

Already have AI code that's inaccessible? Use this refactoring prompt:

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SEO Optimization

Your AI-generated landing page looks stunning. It also doesn't exist to Google.

AI doesn't think about SEO unless you tell it to. No meta tags. No heading hierarchy. No structured data. No semantic HTML that search engines understand.

Here's how to fix that.

The SEO Essentials

Every page needs:

1. Meta Tags

2. Open Graph Tags (for social sharing)

3. Heading Hierarchy

• One <h1> per page (the main topic)
• <h2> for major sections
• <h3> for subsections
• Never skip levels (h1 → h3 is wrong)

4. Semantic Structure

SEO Prompt Template

Add this to any page-level component prompt:

Structured Data for Rich Snippets

Want your pages to appear with ratings, prices, or FAQ dropdowns in search results? You need structured data.

FAQ Schema (for FAQ sections):

Product Schema (for e-commerce):

For complete SEO prompt templates and more schema types, check our SEO-ready landing pages guide.

The SEO Audit Prompt

After generating any page:

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Production Checklist

You've generated, debugged, refactored, and optimized. Now the final gate: is this actually ready to ship?

This checklist has saved me from embarrassing production bugs more times than I can count.

The Pre-Ship Checklist

Build & Errors

• Build completes with no errors (npm run build)
• No TypeScript errors or warnings
• Console is clean in dev tools (no red errors)
• No unused imports or variables

Functionality

• All buttons/links work (click everything)
• Forms submit correctly (test happy path)
• Forms handle errors gracefully (test sad path)
• Loading states appear and disappear correctly
• Empty states are handled (what if there's no data?)

Responsive

• Test on actual mobile device (not just devtools)
• Touch targets are at least 44x44px
• No horizontal scroll on mobile
• Text is readable without zooming

Performance

• Images are optimized (compressed, right format)
• No unnecessarily large dependencies
• Largest Contentful Paint < 2.5s
• Page weight is reasonable (< 1MB for most pages)

Accessibility

• Keyboard navigation works throughout
• Focus states are visible
• Screen reader announces content correctly
• Color contrast passes (use a checker tool)

Security

• No secrets in client-side code
• Forms have proper validation
• User input is sanitized before display
• Auth flows are tested thoroughly

SEO

• Meta tags are present and correct
• OG images display properly when shared
• Heading hierarchy is correct
• Structured data validates (test with Google's tool)

The Final Sanity Check

Before clicking deploy, ask yourself:

1. Would I be embarrassed if my boss/client saw this? If yes, it's not ready.
2. What happens if 1000 users hit this simultaneously? Consider caching and performance.
3. What's the worst thing a user could do? Test that edge case.
4. If this breaks at 2 AM, how hard is it to fix? Error logging matters.

Post-Deploy Monitoring

Shipping isn't the end:

• Set up error tracking (Sentry, LogRocket, etc.)
• Monitor Core Web Vitals
• Watch for user feedback
• Have a rollback plan ready

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Putting It All Together

Here's the complete workflow in action:

Phase 1 gets you working code. Phase 2 gets you reliable code. Phase 3 gets you maintainable code. Phase 4 gets you shippable code.

Skip any phase and you're building on a shaky foundation.

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You Might Also Like

Our cluster articles dive deeper into each part of this workflow:

• Vibe Debugging: 5-Step Workflow That Fixes Any AI UI - Master the debugging phase with prompts and real examples
• Fix AI-Generated Code Errors (Actually Works) - Quick fixes for the 5 most common AI code errors
• 5 Steps to Fix Any AI UI - The iteration workflow that transforms broken UIs
• Vibe Refactoring: 20+ Prompts to Clean Up AI Code - Turn spaghetti into maintainable code
• Generate Tailwind Components with AI - The system for consistent Tailwind output
• AI Accessibility Prompts: WCAG UIs That Pass - Make your AI code accessible
• AI Pages Invisible to Google? Fix It Fast - SEO prompts that get your pages ranked

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Frequently Asked Questions

What is an AI coding workflow?

An AI coding workflow is a systematic process for turning AI-generated code into production-ready applications. It includes four phases: generating initial code, debugging issues, refactoring for maintainability, and preparing for production (accessibility, SEO, security). Unlike one-shot prompting, a workflow treats AI generation as part of a larger development process.

How do I debug AI-generated code effectively?

The most effective approach is the error-forward technique: copy the exact error message and code, feed both back to AI, and ask for a fix with an explanation. Before asking for fixes, have AI explain what the code does—this often reveals the bug immediately. For complex issues, isolate the suspected component into a minimal test case first.

Should I refactor AI-generated code?

Yes, almost always. AI code often works but is difficult to maintain—you'll find repeated logic, hardcoded values, and inconsistent patterns. Refactoring makes the code changeable for future updates. The exception is throwaway prototypes that will be deleted soon anyway.

How do I make AI-generated code accessible?

Use accessibility-focused prompts that require semantic HTML, proper ARIA labels, keyboard navigation, and color contrast. After generation, run an accessibility audit prompt to identify issues. Key checks: all interactive elements must be focusable, forms need proper labels, and color shouldn't be the only way to convey information.

Why does AI-generated Tailwind look inconsistent?

AI picks colors, spacing, and styling randomly unless you constrain it. Define design tokens (exact colors, spacing scale, border radius) upfront and include them in every prompt. Generate base components (buttons, cards, inputs) first, then build complex UIs from those consistent primitives.

How long should the AI coding workflow take?

For a typical component: generation takes 2-5 minutes, debugging 5-15 minutes (if needed), refactoring 5-10 minutes, and production checks 10-20 minutes. Total: 20-50 minutes for a production-ready component. This is still faster than hand-coding, but slower than the marketing hype suggests.

What's the difference between debugging and iterating on AI code?

Debugging fixes broken functionality (errors, crashes, non-working features). Iteration improves working code that doesn't meet requirements (wrong colors, bad layout, missing features). Use debugging when something throws an error or doesn't work. Use iteration when it works but doesn't look or behave how you want.

How do I know when AI code is ready for production?

Run through the production checklist: build passes without errors, all functionality works on desktop and mobile, accessibility audit passes, SEO elements are present, and security basics are covered. If you'd be embarrassed showing it to a client, it's not ready yet.

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Final Thoughts

AI coding tools have fundamentally changed how we build software. But the developers who actually ship products understand something crucial: the tool is only as good as the workflow around it.

Generating code is table stakes. Everyone can do that now. The competitive advantage is in debugging faster, iterating systematically, refactoring intentionally, and shipping confidently.

This workflow won't make AI perfect—nothing will. But it will make AI useful. And useful, shipped code beats perfect, theoretical code every time.

Now stop reading and start building.

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Written by the Fardino Team. We build AI tools for frontend developers. Build with Fardino → and put this workflow to the test.