Database design is tedious work. You map relationships, decide on indexes, write migrations, create factories, and then hope you didn’t forget a foreign key or choose the wrong column type. With Laravel Boost and AI agents, you describe what you need in plain English and get production-ready Eloquent code that understands your existing schema.
Laravel Boost’s database tools let AI agents inspect your current tables, suggest optimal relationships, and generate migrations that won’t conflict with existing data. Combined with the laravel-simplifier agent skill, you get code reviews that catch N+1 queries before they hit production.
This guide shows how to use AI-assisted database design for migrations, relationships, complex queries, and optimization.
:::note[TL;DR]
- Use
get_database_schematool to give AI agents visibility into your current structure - Describe features in plain English: “Users can subscribe to multiple plans with trial periods”
- AI generates migrations with proper foreign keys, indexes, and column types
- Review generated code for business logic edge cases AI cannot know
- Use
laravel-simplifierto catch missing indexes and inefficient queries :::
How Do I Generate Migrations with AI?
Traditional migration writing involves checking existing tables, deciding on column types, and manually typing out schema changes. With Laravel Boost, describe the feature and let the agent handle the implementation.
Scenario: You need a subscription system where users can have multiple subscriptions, each with a trial period, billing cycle, and cancellation status. The agent needs to understand your existing users and plans tables.
In Claude Code with Laravel Boost installed:
> Create a migration for user subscriptions. Users can subscribe to multiple plans. Each subscription should track trial end date, current period end, status (active, cancelled, past_due), and stripe subscription ID. Check my existing users and plans tables first.Boost’s get_database_schema tool automatically provides the agent with your current table structure. The agent sees your users.id type, existing indexes, and any plans table columns. The generated migration will have compatible foreign key types and won’t duplicate existing columns.
<?php
use Illuminate\Database\Migrations\Migration;
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Support\Facades\Schema;
return new class extends Migration
{
public function up(): void
{
Schema::create('subscriptions', function (Blueprint $table) {
$table->id();
$table->foreignId('user_id')->constrained()->cascadeOnDelete();
$table->foreignId('plan_id')->constrained()->cascadeOnDelete();
$table->string('stripe_subscription_id')->unique();
$table->timestamp('trial_ends_at')->nullable();
$table->timestamp('current_period_ends_at');
$table->string('status')->default('active'); // active, cancelled, past_due
$table->timestamp('cancelled_at')->nullable();
$table->timestamps();
// Indexes for common query patterns
$table->index(['user_id', 'status']);
$table->index('stripe_subscription_id');
$table->index('current_period_ends_at');
});
}
public function down(): void
{
Schema::dropIfExists('subscriptions');
}
};The agent added indexes based on likely query patterns: looking up subscriptions by user and status, finding by Stripe ID, and querying by renewal date. You should review these assumptions against your actual application needs.
How Do I Design Complex Relationships with AI?
Many-to-many relationships with pivot data, polymorphic associations, and self-referential tables are easy to describe but tedious to implement correctly.
Scenario: A project management app where tasks can have subtasks (nested), be assigned to multiple users, and belong to a project. Tasks also have comments from users.
> Design the Eloquent relationships for a project management system. Projects have many tasks. Tasks can have subtasks (nested). Tasks can be assigned to multiple users. Users can comment on tasks. Use the database schema to check existing tables.The agent generates the models with relationships:
<?php
namespace App\Models;
use Illuminate\Database\Eloquent\Factories\HasFactory;
use Illuminate\Database\Eloquent\Model;
use Illuminate\Database\Eloquent\Relations\BelongsTo;
use Illuminate\Database\Eloquent\Relations\BelongsToMany;
use Illuminate\Database\Eloquent\Relations\HasMany;
class Task extends Model
{
use HasFactory;
protected $fillable = [
'project_id',
'parent_id',
'title',
'description',
'status',
'due_date',
];
protected $casts = [
'due_date' => 'datetime',
];
// Task belongs to a project
public function project(): BelongsTo
{
return $this->belongsTo(Project::class);
}
// Nested subtasks
public function parent(): BelongsTo
{
return $this->belongsTo(Task::class, 'parent_id');
}
public function children(): HasMany
{
return $this->hasMany(Task::class, 'parent_id');
}
// Task assigned to multiple users
public function assignees(): BelongsToMany
{
return $this->belongsToMany(User::class, 'task_user')
->withPivot('assigned_at', 'assigned_by')
->withTimestamps();
}
// Comments on this task
public function comments(): HasMany
{
return $this->hasMany(Comment::class);
}
}The pivot table migration includes extra context (assigned_at, assigned_by) that business logic often needs but developers forget to add initially.
How Do I Write Complex Queries with AI Assistance?
Eloquent’s query builder can handle complex conditions, but the syntax gets verbose. Describe what you need and let the agent generate optimized queries.
Scenario: Find all users who have active subscriptions, made a purchase in the last 30 days, but haven’t logged in for over a week. You need this for a re-engagement email campaign.
> Write an Eloquent query to find users for a re-engagement campaign: active subscriptions, purchase in last 30 days, no login for 7+ days. Optimize for performance with proper eager loading.<?php
use App\Models\User;
use Illuminate\Support\Carbon;
$usersToReEngage = User::query()
// Users with active subscriptions
->whereHas('subscriptions', function ($query) {
$query->where('status', 'active')
->where('current_period_ends_at', '>', now());
})
// Made a purchase in the last 30 days
->whereHas('orders', function ($query) {
$query->where('status', 'completed')
->where('created_at', '>=', Carbon::now()->subDays(30));
})
// Haven't logged in for 7+ days
->where(function ($query) {
$query->whereNull('last_login_at')
->orWhere('last_login_at', '<', Carbon::now()->subDays(7));
})
// Eager load relationships to avoid N+1
->with(['subscriptions.plan', 'orders' => function ($query) {
$query->latest()->limit(5);
}])
->select(['id', 'name', 'email', 'last_login_at']) // Select only needed columns
->chunk(100, function ($users) {
foreach ($users as $user) {
// Dispatch re-engagement email job
dispatch(new SendReEngagementEmail($user));
}
});The agent included chunk() for memory efficiency with large datasets, eager loading to prevent N+1 queries, and specific column selection to reduce data transfer.
How Do I Optimize Database Performance with AI?
AI agents can suggest indexes and query optimizations, but you should verify recommendations against your actual query patterns.
Scenario: Your application has slow page loads on the dashboard. The agent can analyze your query log and suggest fixes.
> Analyze my recent query log for N+1 problems and missing indexes. Focus on the dashboard route.If you’ve configured logging, the agent uses read_logs to find slow queries. Typical output:
// Issue 1: N+1 on dashboard - missing eager load
User::with(['posts', 'comments', 'profile'])->find($id);
// Issue 2: Missing index on posts.user_id
// Add: $table->index('user_id'); to posts migration
// Issue 3: Count query without caching
// Cache: Cache::remember('posts.count', 3600, fn() => Post::count());What Are the Limits of AI-Assisted Database Design?
AI agents excel at boilerplate and pattern recognition but struggle with domain-specific business logic.
What AI handles well:
- Standard CRUD migrations
- Common relationship types (hasMany, belongsToMany, morphTo)
- Index suggestions for typical query patterns
- Query optimization for performance
What requires human review:
- Business rules that aren’t standard (e.g., “subscriptions pause after 3 failed payments”)
- Data integrity constraints unique to your domain
- Regulatory compliance (GDPR data retention, HIPAA audit trails)
- Edge cases in multi-tenant or sharded setups
Always review generated migrations for business logic correctness. Run them in a staging environment first.
How Do I Generate Factories and Seeders with AI?
Testing requires realistic data. AI can generate factories with proper faker patterns and seeders that create meaningful relationships.
> Create a factory and seeder for the Subscription model with realistic test data. Include relationships to users and plans.<?php
namespace Database\Factories;
use App\Models\Plan;
use App\Models\Subscription;
use App\Models\User;
use Illuminate\Database\Eloquent\Factories\Factory;
class SubscriptionFactory extends Factory
{
protected $model = Subscription::class;
public function definition(): array
{
$trialEnds = $this->faker->boolean(70)
? null // 70% chance of past trial
: now()->addDays($this->faker->numberBetween(1, 14));
return [
'user_id' => User::factory(),
'plan_id' => Plan::factory(),
'stripe_subscription_id' => 'sub_' . $this->faker->uuid(),
'trial_ends_at' => $trialEnds,
'current_period_ends_at' => now()->addMonth(),
'status' => $this->faker->randomElement(['active', 'active', 'active', 'cancelled', 'past_due']),
'cancelled_at' => null,
];
}
public function cancelled(): static
{
return $this->state(fn (array $attributes) => [
'status' => 'cancelled',
'cancelled_at' => now(),
]);
}
public function onTrial(): static
{
return $this->state(fn (array $attributes) => [
'trial_ends_at' => now()->addDays(14),
'status' => 'active',
]);
}
}Summary
- Laravel Boost gives AI agents database introspection via
get_database_schemaandexecute_read_only_query - Describe features in plain English, get migrations with proper types, indexes, and foreign keys
- Complex relationships (nested, polymorphic, many-to-many with pivot data) generate correctly when described clearly
- Query optimization includes eager loading, chunking, and column selection automatically
- Always review generated code for business logic edge cases AI cannot know
FAQ
Will AI-generated migrations conflict with my existing data? Boost’s schema inspection helps, but always test migrations on a copy of production data. AI cannot see your actual data patterns or know about legacy constraints not reflected in the schema.
Can AI help with database refactoring or large migrations? Yes. Describe the goal: “Split the monolithic orders table into orders and order_items.” The agent can generate the full migration sequence with data preservation. Review carefully and test exhaustively.
How do I prevent AI from suggesting unsafe queries? Boost only exposes read-only query execution. For destructive operations, the agent generates code for you to review before running. Never blindly execute AI-generated migrations on production.
Does AI understand my custom Eloquent scopes and mutators?
If the files are in your project, Boost can read them and include them in context. Mention them explicitly: “I have a scopeActive() on the Subscription model, use that.”
Can AI optimize existing queries in my codebase?
Use the laravel-simplifier skill: “Review recent changes and optimize slow queries.” The agent can read your query logs (if configured) and suggest specific indexes or eager loading additions.
What to Read Next
- Laravel OpenAI Integration: Building Your First AI Feature - Add content generation and AI classification to your application
- Laravel Vector Search and RAG Implementation - Build AI-powered search with embeddings
- Laravel firstOrCreate(): Find or Create a Record - Deep dive on this useful Eloquent method
