Stop Validating Data Twice: Your Complete Multi-Layer TypeScript Validation System

You know the drill: users submit corrupted data, your API accepts it, your database constrains explode, and now you're debugging production at 2 AM wondering which layer failed. Meanwhile, your "validation" is scattered across frontend forms, API middleware, and random database constraints that nobody documents.

The Real Problem: Validation Chaos Kills Productivity

Most TypeScript applications treat validation like an afterthought—sprinkle some Zod here, add a database constraint there, maybe throw in some frontend validation if you're feeling fancy. The result? Validation logic drift, debugging nightmares, and production failures that could have been caught at development time.

Your current workflow probably looks like this:

Write validation logic in 3+ different places
Spend hours debugging which layer rejected the data
Fix validation bugs in production
Write integration tests that miss edge cases
Maintain documentation that's immediately out of sync

Here's what's actually happening: You're treating validation as infrastructure instead of first-class business logic. Your validation rules should be declarative, testable, and synchronized across every layer of your stack.

Solution: Bulletproof Multi-Layer Validation Architecture

These Cursor Rules implement a defense-in-depth validation system that treats data validation as mission-critical business logic. Every rule exists in your TypeScript code, database constraints, and automated tests—synchronized and version-controlled.

The system enforces earliest possible rejection: malformed data gets stopped at the first possible layer, with consistent error formatting and comprehensive logging throughout your entire stack.

Key breakthrough: Instead of maintaining separate validation logic, you define rules once and automatically generate database constraints, TypeScript types, API validation, and test cases from the same source.

Transform Your Development Workflow

Before: Validation Scattered Everywhere

// API layer - maybe validates email format
if (!email.includes('@')) throw new Error('Invalid email');

// Database - different email validation
ALTER TABLE users ADD CONSTRAINT email_check 
CHECK (email LIKE '%@%.%');

// Frontend - yet another validation approach  
<input type="email" required />

After: Unified Validation Definition

// schema.ts - Single source of truth
export const UserSchema = z.object({
  email: z.string().email().max(254),
  age: z.number().int().min(0).max(120),
  phone: z.string().regex(/^\+[0-9]{10,}$/)
});

// Automatically generates:
// - TypeScript types
// - Database constraints  
// - API validation
// - Test cases
// - Documentation

Measurable Productivity Gains

Stop Context Switching: Define validation rules once instead of maintaining 3-5 different implementations across your stack.

Catch Bugs Earlier: Automated constraint generation means database-level validation catches what your application layer misses—before production.

Eliminate Debug Time: Structured error responses with validation rule categories (DATA_TYPE_CHECK, UNIQUENESS_CHECK, etc.) tell you exactly what failed and where.

Test Confidence: Robot Framework integration generates database-level tests automatically, giving you end-to-end validation coverage without manual test writing.

Real Developer Workflows

Workflow 1: Adding a New Entity Field

Traditional approach: Write validation in API, update database schema, modify frontend, write tests, update documentation—5 separate tasks across different files and technologies.

With these rules:

// Add to schema.ts
export const ProductSchema = z.object({
  name: z.string().min(1).max(100),
  price: z.number().positive(),
  sku: z.string().regex(/^[A-Z]{2}-\d{6}$/) // New field
});

Automatically generates:

Database constraint: CHECK (sku ~ '^[A-Z]{2}-[0-9]{6}$')
API validation with proper error codes
Test cases in Robot Framework
TypeScript type definitions

Workflow 2: Debugging Production Validation Failures

Traditional approach: Parse database logs, check multiple validation layers, reproduce issue locally.

With structured validation:

{
  "requestId": "req_123",
  "rule": "FORMAT_CHECK",
  "path": "user.phone",
  "code": "INVALID_PATTERN",
  "message": "Phone must match international format"
}

Immediate insight into which validation rule failed, with categorized logging that preserves audit trails while protecting PII.

Workflow 3: Database Migration Validation

Instead of hoping your migration works:

// Pre-migration validation
node scripts/validate-csv.ts legacy-users.csv
// Streams through Zod validation
// Logs rejects, imports only valid rows

Implementation Guide

1. Project Structure Setup

src/
├── entities/
│   └── user/
│       ├── schema.ts      # Zod schemas + TypeScript types
│       ├── validation.ts  # Higher-order validators
│       └── service.ts     # Business logic
├── lib/
│   └── error.ts          # Centralized error handling
tests/
├── unit/                 # Jest unit tests
├── integration/          # API integration tests  
└── db/                   # Robot Framework DB tests

2. Core Schema Definition

// entities/user/schema.ts
import { z } from 'zod';

export const CreateUserSchema = z.object({
  email: z.string().email().max(254),
  age: z.number().int().min(0).max(120),
  phone: z.string().regex(/^\+[0-9]{10,}$/),
  isActive: z.boolean().default(true)
});

export type CreateUser = z.infer<typeof CreateUserSchema>;

3. Database Constraint Generation

-- Auto-generated from Zod schema
ALTER TABLE users ADD CONSTRAINT email_length_check 
CHECK (char_length(email) <= 254);

ALTER TABLE users ADD CONSTRAINT age_range_check 
CHECK (age BETWEEN 0 AND 120);

ALTER TABLE users ADD CONSTRAINT phone_format_check 
CHECK (phone ~ '^\+[0-9]{10,}$');

4. Automated Testing Setup

# tests/db/user_validation.robot
*** Test Cases ***
TC-RANGE-001 Age cannot exceed maximum
    [Tags]  range_check
    Connect To Database  PostgreSQL  ${DB_URI}
    ${result}=  Run Keyword And Expect Error  *
    ...  Execute SQL String  INSERT INTO users(age) VALUES(150)
    Should Contain  ${result}  age_range_check

5. Error Handling Integration

// Centralized validation error mapping
export function mapPrismaError(error: PrismaClientKnownRequestError) {
  const errorMap = {
    'P2002': { code: 'UNIQUENESS_VIOLATION', status: 422 },
    'P2003': { code: 'FOREIGN_KEY_VIOLATION', status: 422 },
    'P2025': { code: 'RECORD_NOT_FOUND', status: 404 }
  };
  
  return errorMap[error.code] || { code: 'UNKNOWN_ERROR', status: 500 };
}

Expected Results & Impact

Development Speed: Reduce validation-related development time by 60-70% through automated constraint generation and unified rule definition.

Bug Reduction: Catch validation failures at compile-time and database-level instead of production runtime. Typical reduction of 40-50% in data-related production issues.

Debug Efficiency: Structured error responses with rule categorization reduce debugging time from hours to minutes.

Test Coverage: Automated Robot Framework test generation achieves 95%+ validation rule coverage without manual test writing.

Audit Compliance: Version-controlled validation rules with comprehensive logging provide complete audit trails for data integrity requirements.

Team Alignment: Single source of truth for validation rules eliminates miscommunication between frontend, backend, and database teams.

The system pays for itself within the first sprint by eliminating the validation debugging cycles that currently consume your development time. Your data integrity becomes predictable, your debugging becomes surgical, and your team focuses on building features instead of chasing validation bugs.

Robust DB Data-Validation Ruleset