AI Code Generation: From Prototype to Production-Ready Code

AI-assisted code generation fundamentally changes how developers work. Rather than replacing human developers, AI tools amplify productivity by automating repetitive tasks, generating boilerplate, and suggesting patterns. When combined with proper workflows and quality controls, AI code generation cuts development time whilst maintaining production-quality standards.

This comprehensive guide covers practical AI code generation for Laravel, Shopify, and WordPress projects, with real-world examples and proven safety patterns.

Understanding AI Code Generation

What AI Does Well

AI excels at pattern recognition and repetition. It can generate:

Structural Code: Migrations, models, controllers, routes, configurations Boilerplate: CRUD operations, API endpoints, authentication scaffolding Repetitive Components: Form fields, table columns, list items, card layouts Test Coverage: Unit tests, feature tests, integration tests for existing code Documentation: Inline comments, README files, API documentation Transformations: Converting between languages, frameworks, or patterns

What Requires Human Oversight

AI struggles with:

Business Logic: Domain-specific rules and constraints Security Implementation: Authentication, authorisation, encryption Performance Optimisation: Database indexing strategies, caching layers Architectural Decisions: System design, technology choices Context-Heavy Code: Code requiring deep understanding of the broader application

The key is using AI as a productivity tool whilst maintaining human responsibility for critical decisions.

High-ROI Code Generation Opportunities

Laravel Development

Database Layer:

Generate a Laravel migration and model for a blog post system.

Requirements:
- Posts table with: title, slug, content, excerpt, status, published_at
- Categories table with: name, slug, description
- Many-to-many relationship between posts and categories
- Soft deletes on posts
- Indexes on slug and published_at fields
- Model should include relationships, scopes, and accessors

AI generates:

// Migration
public function up()
{
    Schema::create('posts', function (Blueprint $table) {
        $table->id();
        $table->string('title');
        $table->string('slug')->unique();
        $table->text('content');
        $table->text('excerpt')->nullable();
        $table->enum('status', ['draft', 'published', 'archived'])->default('draft');
        $table->timestamp('published_at')->nullable();
        $table->timestamps();
        $table->softDeletes();

        $table->index('slug');
        $table->index('published_at');
    });

    Schema::create('categories', function (Blueprint $table) {
        $table->id();
        $table->string('name');
        $table->string('slug')->unique();
        $table->text('description')->nullable();
        $table->timestamps();
    });

    Schema::create('category_post', function (Blueprint $table) {
        $table->foreignId('category_id')->constrained()->onDelete('cascade');
        $table->foreignId('post_id')->constrained()->onDelete('cascade');
        $table->primary(['category_id', 'post_id']);
    });
}

// Model
class Post extends Model
{
    use HasFactory, SoftDeletes;

    protected $fillable = [
        'title', 'slug', 'content', 'excerpt', 'status', 'published_at'
    ];

    protected $casts = [
        'published_at' => 'datetime',
    ];

    // Relationships
    public function categories()
    {
        return $this->belongsToMany(Category::class);
    }

    // Scopes
    public function scopePublished($query)
    {
        return $query->where('status', 'published')
                     ->whereNotNull('published_at')
                     ->where('published_at', '<=', now());
    }

    // Accessors
    public function getExcerptAttribute($value)
    {
        return $value ?? Str::limit(strip_tags($this->content), 150);
    }
}

Learn more about Laravel Eloquent relationships and database optimisation.

API Controllers:

Generate a Laravel API controller for posts with:
- Resource responses
- Validation
- Policy authorisation
- Query optimisation
- Error handling

This generates production-ready controllers with proper structure. Review Laravel API design patterns for best practices.

Shopify Theme Development

Liquid Sections:

Generate a Shopify section for a featured products carousel.

Settings:
- Collection selector
- Number of products (1-10)
- Layout: grid or carousel
- Show prices: yes/no
- Button text (customisable)

Output as complete section with schema.

AI produces the Liquid template, CSS, and JSON schema. Combine with Shopify theme performance strategies.

Shopify Scripts:

Generate a Shopify Script for bulk discount:
- Buy 3+ items, get 10% off
- Buy 5+ items, get 15% off
- Buy 10+ items, get 20% off
Apply to specific collection only

Review Shopify API integration for advanced customisations.

WordPress Development

Custom Post Types:

Generate WordPress custom post type registration for "Case Studies" with:
- Custom taxonomies: Industry, Service
- Custom fields (ACF): Client name, project duration, results
- Archive template structure
- Single template structure

AI generates the registration code, template hierarchy, and field definitions. See WordPress hook system for integration patterns.

Plugin Scaffolding:

Generate WordPress plugin boilerplate for "Advanced Contact Form" with:
- Admin settings page
- Shortcode support
- Email notifications
- Database table for submissions
- AJAX form submission

Combine with WordPress plugin performance optimisation.

Establishing Effective Prompt Templates

Structured Request Format

Use consistent formatting for predictable outputs:

Task: [What you want generated]
Context: [Framework version, dependencies, constraints]
Requirements: [Specific features, patterns, standards]
Output Format: [Code only, with comments, with tests]
Constraints: [What to avoid, what to include]

Laravel Controller Example

Task: Generate Laravel resource controller for Product model
Context: Laravel 12, using API resources, JSON responses
Requirements:
- Index with pagination (15 per page)
- Store with validation
- Show with relationships (category, images)
- Update with authorisation
- Destroy with soft delete
Output Format: Complete controller with comments
Constraints:
- Use dependency injection
- Include type hints
- Follow PSR-12

This structured approach ensures consistent, production-ready code. Review prompt engineering best practices for advanced techniques.

TypeScript Component Example

Task: Convert React component to TypeScript
Context: React 18, using hooks
Requirements:
- Preserve all functionality
- Add proper type definitions
- Include prop types and return types
- Add JSDoc comments
Output Format: Complete TypeScript component
Constraints:
- Use functional components
- Avoid 'any' type
- Enable strict mode compatibility

Code Quality and Safety

Automated Review Tooling

Never commit AI-generated code without validation:

PHP/Laravel:

# Static analysis
./vendor/bin/phpstan analyse --level=8

# Code style
./vendor/bin/php-cs-fixer fix --dry-run

# Security scanning
./vendor/bin/security-checker security:check

# Tests
php artisan test

JavaScript/TypeScript:

# Linting
npm run lint

# Type checking
npm run type-check

# Tests
npm test

# Build verification
npm run build

WordPress:

# WordPress Coding Standards
phpcs --standard=WordPress

# PHP compatibility
phpcs -p . --standard=PHPCompatibilityWP

# Security scanning
npm run security-check

Security Checklist

Before committing AI-generated code, verify:

• No hardcoded credentials or API keys
• Input validation on all user data
• SQL injection prevention (parameterised queries)
• XSS protection (escaped output)
• CSRF protection on forms
• Proper authorisation checks
• Secure file upload handling
• Rate limiting on public endpoints

Learn about Laravel testing strategies.

Performance Validation

Check AI-generated code for:

Database Queries:

• N+1 query problems
• Missing indexes
• Eager loading opportunities
• Unnecessary SELECT *

Caching:

• Cache frequently accessed data
• Implement cache invalidation
• Use appropriate cache drivers

Resource Usage:

• Memory-efficient operations
• Proper pagination
• Background job usage for heavy tasks

Review Laravel performance optimisation and WordPress caching strategies.

Workflow Integration

IDE Integration

VS Code with GitHub Copilot:

// settings.json
{
  "github.copilot.enable": {
    "*": true,
    "yaml": false,
    "plaintext": false
  },
  "github.copilot.advanced": {
    "debug.overrideEngine": "gpt-4",
    "inlineSuggestEnable": true
  }
}

Cursor IDE:

• Built-in AI chat
• Context-aware suggestions
• Multi-file editing

JetBrains AI Assistant:

• Code generation
• Refactoring suggestions
• Documentation generation

CLI Tools

Custom Laravel Artisan Command:

class GenerateAIScaffolding extends Command
{
    protected $signature = 'ai:scaffold {model} {--type=crud}';

    public function handle(AIService $ai)
    {
        $model = $this->argument('model');
        $type = $this->option('type');

        $prompt = PromptTemplate::scaffold($model, $type);
        $code = $ai->generate($prompt);

        // Validate and save
        if ($this->validateCode($code)) {
            $this->saveFiles($code);
            $this->info("Scaffolding generated for {$model}");
        }
    }
}

Learn about Laravel custom Artisan commands.

Git Hooks

Pre-commit Validation:

#!/bin/bash
# .git/hooks/pre-commit

# Check for AI-generated code markers
if git diff --cached | grep -q "GENERATED_BY_AI"; then
    echo "AI-generated code detected. Running validation..."

    # Run tests
    php artisan test

    # Run static analysis
    ./vendor/bin/phpstan analyse

    if [ $? -ne 0 ]; then
        echo "Validation failed. Please review AI-generated code."
        exit 1
    fi
fi

Real-World Implementation Cases

Case Study 1: Laravel Admin Panel Generator

Challenge: Build CRUD interfaces for 15+ models quickly whilst maintaining consistency.

AI Workflow:

1. Define model specifications in YAML
2. Generate controllers using AI
3. Generate views with consistent styling
4. Generate form validation rules
5. Generate API resources
6. Automated test generation

Prompt Template:

Generate Laravel admin CRUD for ${modelName}.

Model specification:
${modelSpec}

Generate:
1. Controller with index, create, store, edit, update, destroy
2. Form request validation
3. API resource
4. Blade views following admin template
5. Feature tests

Follow Laravel best practices and include proper authorisation.

Results:

• 70% time reduction for CRUD generation
• Consistent code patterns across all admin sections
• Comprehensive test coverage (80%+)
• Zero security vulnerabilities in generated code

Key Learnings:

• Template-based generation ensures consistency
• Validation rules require human review
• Business logic still needs manual implementation
• AI excels at repetitive structure, not unique requirements

Case Study 2: Shopify Theme Section Library

Challenge: Create 20+ reusable Shopify sections with consistent settings and performance optimisation.

AI Workflow:

1. Define section requirements and settings
2. Generate Liquid template with AI
3. Add JSON schema for Shopify customiser
4. Generate accompanying CSS/JS
5. Implement lazy loading and performance optimisations
6. Generate section documentation

Prompt Template:

Generate Shopify section for ${sectionType}.

Requirements:
${requirements}

Include:
- Liquid template with proper loops and conditionals
- Schema for customiser with all settings
- CSS following BEM methodology
- Vanilla JavaScript (no frameworks)
- Image lazy loading
- Performance optimisations

Output as complete .liquid file.

Results:

• 50 development hours saved across project
• All sections achieved 90+ performance scores
• Consistent API across all sections
• Client could customise without developer involvement

Key Learnings:

• AI handles Liquid syntax well with good prompts
• Performance patterns need explicit specification
• Schema generation saves significant time
• Human review essential for accessibility

Review Shopify Liquid fundamentals and customisation techniques.

Case Study 3: WordPress Plugin Boilerplate Generator

Challenge: Rapid prototyping of WordPress plugins with consistent architecture.

AI Workflow:

1. Define plugin requirements
2. Generate plugin header and structure
3. Generate admin pages and settings
4. Generate custom post types and taxonomies
5. Generate shortcodes and widgets
6. Generate activation/deactivation hooks

Prompt Template:

Generate WordPress plugin "${pluginName}".

Description: ${description}

Features:
${features}

Include:
- Proper plugin header
- OOP structure with namespaces
- Admin settings page
- Activation hooks
- Uninstall cleanup
- Security: nonces, capabilities, sanitisation
- i18n ready

Follow WordPress Coding Standards.

Results:

• Plugin prototypes in 2 hours vs 2 days
• Consistent security patterns
• Easy maintenance due to structured code
• Rapid iteration on client feedback

Key Learnings:

• WordPress conventions need explicit specification
• Security patterns must be included in prompts
• i18n considerations often require manual addition
• Plugin activation/deactivation logic needs review

Explore WordPress performance strategies and hosting considerations.

Advanced Patterns

Test-Driven AI Generation

Generate tests first, then code:

Generate PHPUnit tests for a Laravel service that processes customer orders.

Requirements:
- Test successful order processing
- Test validation failures
- Test payment gateway errors
- Test inventory checks
- Test email notifications

Then generate the service class that passes these tests.

This approach ensures:

• Code meets specified requirements
• Edge cases are considered
• Refactoring safety
• Documentation through tests

Incremental Generation

For complex features, generate in layers:

Layer 1: Data Structure

Generate database schema for e-commerce order system.
Include: orders, order_items, customers, addresses, payments

Layer 2: Models

Generate Laravel models with relationships for the above schema.

Layer 3: Business Logic

Generate service classes for:
- Order creation
- Payment processing
- Inventory management

Layer 4: API Layer

Generate API controllers and resources for the order system.

This incremental approach maintains context and allows validation at each stage.

Context-Aware Generation

Use RAG (Retrieval-Augmented Generation) to include project-specific patterns:

Generate a new Laravel controller following our project patterns.

Project context:
${projectGuidelines}
${existingControllerExamples}

Generate controller for: ${modelName}

Learn about RAG systems implementation and MCP setup.

Cost and Time Optimisation

Token Usage Strategies

Minimise Context:

// Less efficient
"Here's my entire 5000-line codebase... now generate a new controller"

// More efficient
"Generate controller following this pattern: [paste single example controller]"

Reuse Generations: Cache frequently generated patterns:

const cache = new Map();

async function generateCode(prompt: string): Promise<string> {
  const cacheKey = hash(prompt);

  if (cache.has(cacheKey)) {
    return cache.get(cacheKey);
  }

  const code = await ai.generate(prompt);
  cache.set(cacheKey, code);

  return code;
}

Batch Operations: Generate multiple related files in one request:

Generate these Laravel files in one response:
1. Model: Product
2. Migration for products table
3. Factory for testing
4. Resource for API
5. FormRequest for validation

Ensure all files work together correctly.

Time Savings Measurement

Track AI-generated code effectiveness:

class AIGenerationMetric
{
    public function track(string $type, float $timeSpent, bool $requiredEdits)
    {
        DB::table('ai_metrics')->insert([
            'generation_type' => $type,
            'time_spent_minutes' => $timeSpent,
            'required_human_edits' => $requiredEdits,
            'created_at' => now()
        ]);
    }

    public function getROI(): array
    {
        return DB::table('ai_metrics')
            ->select(
                'generation_type',
                DB::raw('AVG(time_spent_minutes) as avg_time'),
                DB::raw('SUM(CASE WHEN required_human_edits THEN 1 ELSE 0 END) * 100.0 / COUNT(*) as edit_rate')
            )
            ->groupBy('generation_type')
            ->get()
            ->toArray();
    }
}

Best Practices Summary

Do:

Don’t:

Future of AI Code Generation

Emerging Trends

Multimodal Generation: Convert designs (Figma, Sketch) directly to code Context-Aware IDEs: AI that understands entire project architecture Automated Refactoring: AI suggests and implements code improvements Test Generation: Comprehensive test suites from requirements Documentation Generation: Auto-updated docs from code changes

Preparing for the Future

• Stay current with AI tool capabilities
• Develop strong prompt engineering skills
• Maintain focus on architecture and design
• Build robust code review processes
• Invest in automated testing infrastructure

Key Takeaways

• AI code generation excels at patterns and repetition
• Human oversight remains essential for quality and security
• Structured prompts produce consistent, predictable outputs
• Automated validation (tests, linters, security scans) is non-negotiable
• ROI is highest for boilerplate, CRUD, and scaffolding
• Integration with existing workflows maximises adoption
• Continuous measurement and improvement is crucial

Whether you’re building Laravel applications, customising Shopify themes, or developing WordPress plugins, AI code generation offers tangible productivity gains when applied strategically with proper safeguards.

Complete Series Resources

This concludes our AI in Web Development series:

1. Introduction to AI in Web Development – AI fundamentals and applications
2. Setting up MCP – Memory and context management
3. RAG Systems – Retrieval-augmented generation
4. Prompt Engineering – Writing effective prompts
5. AI Code Generation (this post) – Automated scaffolding and boilerplate

For platform-specific deep dives:

• Laravel Mastery Series
• Shopify Essentials Series
• WordPress Performance Series

Ready to implement AI code generation in your development workflow? Contact us to discuss integration strategies and custom tooling for your team.