AI API integrations often fail at runtime due to unexpected response formats, missing fields, or type mismatches. TypeScript's sophisticated type system can catch many of these issues during development, but effective use requires understanding how to model AI responses and validate runtime data against compile-time types.
Modeling AI Responses
LLM responses are inherently unpredictable, but we can define expected structures that improve reliability. TypeScript interfaces describe the shape of valid responses, while validation libraries like Zod ensure runtime data matches these shapes. This combination provides both development-time checking and runtime safety, catching issues before they propagate through your application.
- Define strict interfaces for structured outputs like JSON responses from function calling
- Use discriminated unions to represent multiple possible response types
- Implement runtime validation with libraries like Zod to verify API responses match expected types
- Create type guards that narrow types safely when handling uncertain data
- Leverage TypeScript's satisfies operator to validate complex configuration objects
Error Handling Patterns
Type-safe error handling ensures your application gracefully handles AI service failures. Result types that explicitly model success and failure cases force developers to handle errors at compile time. This prevents uncaught exceptions that crash applications and provides better error messages to users when AI services misbehave.
Developer Experience Benefits
Beyond runtime safety, strong typing improves developer productivity. IDE autocomplete suggests valid fields and methods. Type errors surface immediately during development rather than during testing. Refactoring becomes safer as the compiler identifies all affected code. These benefits compound over time as AI integrations grow more complex.