TypeScript Patterns for AI-Powered Applications in Production
Type-safe tool calling, streaming response types, Zod schema inference, and error boundaries. The TypeScript patterns that make AI apps reliable.
Building AI features is easy. Building AI features that are type-safe, maintainable, and production-ready is hard. Here are the patterns we use.
Pattern 1: Type-Safe Tool Definitions
Use Zod schemas that double as runtime validators and type generators for AI tool calling.
Pattern 2: Discriminated Unions for AI Responses
Model AI responses with discriminated unions to force explicit handling of every case.
Pattern 3: Streaming Type Guards
Use type guards to narrow stream chunks for safe consumption of streamed responses.
Pattern 4: Error Boundaries for AI Components
Wrap AI-dependent components in specialized error boundaries that handle rate limits, timeouts, and model unavailability differently.
Pattern 5: Generic Model Wrappers
Create typed wrappers around model calls that enforce your team's conventions for retries, logging, and error handling.
These patterns add minimal overhead but dramatically reduce bugs and maintenance burden in production AI applications.