AI Coding Standards

TypeScript Best Practices for AI Application Development

Strict typing patterns for LLM outputs, Zod schemas for AI responses, discriminated unions for async states, and environment variable safety in production AI apps.

Muhammad TalhaFounder & Lead Engineer, Devs & Logics
May 18, 202510 min read

Why TypeScript is Essential for AI SaaS

LLMs return unstructured data. Users send unexpected inputs. External APIs break. TypeScript with strict mode and Zod catches entire categories of runtime bugs before they reach production — critical when every unhandled error costs you a customer.

1. Enable Strict Mode

In tsconfig.json: "strict": true, "noUncheckedIndexedAccess": true, "noImplicitReturns": true. This is non-negotiable for production AI apps.

2. Type LLM Responses with Zod + generateObject

Use the Vercel AI SDK's generateObject with a Zod schema to get fully typed, validated AI outputs. Never trust raw LLM JSON — always validate through a schema.

3. Discriminated Unions for AI States

Model your AI request lifecycle as a discriminated union: idle | loading | success | error. TypeScript forces you to handle every case in your UI, preventing the "blank screen" bug when AI calls fail.

4. Type-Safe Environment Variables

Parse process.env through a Zod schema at startup. Your app fails fast with a clear error if a required env var is missing — instead of a cryptic 500 error in production.

5. Avoid Common Anti-Patterns

Never use as any. Never use type assertions without runtime validation. Avoid @ts-ignore — use @ts-expect-error with a comment. Don't use object or {} as types.

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