How I Use AI to Build Websites Faster

The Reality of AI in Web Development

Every week, someone asks me if AI is going to replace web developers. The honest answer: no—but it's already changed how I work. I ship projects faster, write better code, and spend more time on problems that actually matter.

I've been using AI tools seriously for over two years now. Not as a novelty, but as an integral part of my development workflow. The results have been transformative—but not in the "write my entire website" way that headlines suggest.

Let me show you exactly how I integrate AI into real client projects, what works, what doesn't, and the mistakes I made learning these lessons.

My AI-Assisted Workflow

Here's how AI fits into my actual development process, not a theoretical workflow, but what I do every day on client projects.

1. Project Understanding (Still 100% Human)

   Discovery calls, requirement gathering, understanding the business problem—this remains entirely human work. AI can't replace the judgment needed to understand what a client actually needs versus what they're asking for.

2. Architecture & Planning (AI-Assisted Research)

   When evaluating technology choices or architectural approaches, I use AI to quickly research options, understand tradeoffs, and identify potential issues I might not have considered.

3. Code Generation (Where AI Shines)

   Writing boilerplate, creating database schemas, building CRUD operations, generating tests—AI handles these quickly while I focus on business logic and user experience.

4. Review & Refinement (Critical Human Step)

   Every line of AI-generated code gets reviewed. I understand it, test it, and often improve it. The AI provides a strong first draft; I provide the quality assurance.

My Primary Tool: Claude Code

I've tried many AI coding tools over the past two years. These days, I work almost exclusively with Claude Code—Anthropic's CLI tool that runs directly in my terminal alongside my code.

Why Claude Code Works for Me

Unlike browser-based AI assistants, Claude Code has direct access to my project files. It can read my codebase, understand the existing patterns, and make changes in place. This eliminates the constant copy-paste cycle that makes other AI tools feel clunky.

Here's what I use it for daily:

• Exploring unfamiliar codebases and understanding how pieces connect
• Writing new features that match existing project conventions
• Debugging issues by letting Claude trace through the actual code
• Refactoring with confidence—Claude can update all the related files
• Writing tests that actually cover the edge cases I care about
• Documentation that stays accurate because Claude reads the source

The Workflow

My typical session: I open a terminal in my project directory, start Claude Code, and describe what I'm trying to accomplish. Claude reads the relevant files, proposes an approach, and makes the changes. I review everything before it touches the filesystem, but the back-and-forth is remarkably efficient.

For quick questions or general research, I'll still use Claude in a browser. But for actual development work? Claude Code has become indispensable.

Real Project Example

Let me walk through a recent project where Claude Code meaningfully accelerated delivery.

The Project

A franchise education company needed a custom WordPress admin dashboard for their franchisees. The existing interface used raw ACF options forms—functional, but confusing for non-technical franchise owners. They needed a branded, intuitive replacement that preserved all the existing functionality.

Traditional estimate: 4 weeks. Actual delivery: under 3 weeks, including revisions.

Where Claude Code Helped

Where I Stayed Hands-On

• All client communication and requirement clarification
• User experience decisions—what to show, what to hide, how to organize
• Prototyping and iterating based on client feedback
• Testing across the multisite network
• Final code review and security checks before deployment

What AI Gets Wrong

AI assistance isn't magic. Here are patterns I've learned to watch for:

Specific Problems I've Encountered

• Outdated patterns: AI suggests jQuery when the project uses vanilla JS
• Security oversights: Generated code that didn't properly sanitize inputs
• Performance issues: N+1 queries that looked fine in isolation
• Over-engineering: Complex solutions when simple ones would work
• Missing context: Code that ignores existing project conventions

The solution isn't to avoid AI—it's to maintain the expertise needed to catch these issues. AI is a powerful tool for developers, not a replacement for development skills.

Time Savings Breakdown

Based on tracking my last 10 projects, here's where AI saves the most time:

Notice that client communication time doesn't decrease. That's intentional—relationships and understanding requirements still require human attention. The time saved on execution gives me more capacity for the work that matters most.

Getting Started

If you're a developer looking to integrate AI effectively, here's my advice:

1. Start with code review. Have AI explain unfamiliar code before you try generating new code. This builds trust in what it understands.
2. Use it for boilerplate first. Low-risk tasks like generating CRUD operations or form validation are great starting points.
3. Always review generated code. Treat it like a pull request from a junior developer—helpful but needs oversight.
4. Learn to write good prompts. Specific context leads to better output. Include relevant constraints, conventions, and requirements.
5. Keep your skills sharp. The developers who benefit most from AI are those who can evaluate and improve its output.

AI makes good developers faster. It doesn't make non-developers into developers. The expertise to know what to build—and whether the build is correct—remains essential.

My working principle