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From Pain Points to Production-Ready Code
See how recommendations and guardrails solve real engineering problems
AI generates code that looks 95% right but has missing edge cases, security holes, and performance issues that only break under load.
Recommendation
Always Validate AI Suggestions Before Merging
Treat all AI-generated code as untrusted until validated through manual review, tests, and verification workflows.
Implement with Guardrails
- Prevent Missing Edge Case Testing
- Prevent Insufficient Test Coverage
- Prevent Missing Security Tests
AI confidently invents non-existent API endpoints, deprecated methods, or phantom features that seem plausible but are fundamentally broken.
Recommendation
Ground AI with Current Schema and API Specs
Feed exact database schemas and OpenAPI specs into context to stop AI from guessing. Convert hallucinations into context-aware code.
Implement with Guardrails
- Prevent Data Type Mismatch in API Integration
- Prevent Breaking API Changes
- Prevent Missing Request Validation
AI-generated migration scripts hallucinate schema details or miss implicit dependencies, causing silent data corruption in production.
Recommendation
Use TDD with AI Pair Programming
Define test contracts first. Make AI write code that passes comprehensive test suites covering edge cases and failure modes.
Implement with Guardrails
- Prevent Data Corruption in AI-Generated Migrations
- Prevent Type Coercion Errors in Batch Processing
- Prevent Silent Data Truncation
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Built using AI-augmented development. See the code, follow the commits, learn how modern teams ship fast.
The Philosophy:
Start with TypeScript files, not databases. UI can be rebuilt in minutes. Ship fast, validate, then optimize. This is modern rapid prototyping.
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