TypeScript's type system is powerful enough to catch most runtime errors at compile time. But most TypeScript codebases use only 20% of what's available. Here are the patterns that make TypeScript actually earn its keep in an AI SaaS project.
Typed API Responses
Never use any for API responses. Define the shape and validate at the boundary.
// types/api.ts
export type ApiResponse<T> =
| { success: true; data: T }
| { success: false; error: string; code?: string }
// In your API route
export async function GET(): Promise<Response> {
try {
const users = await db.user.findMany()
return Response.json({ success: true, data: users } satisfies ApiResponse<User[]>)
} catch (err) {
return Response.json(
{ success: false, error: "Failed to fetch users" } satisfies ApiResponse<never>,
{ status: 500 }
)
}
}
// In your client
async function fetchUsers(): Promise<User[]> {
const res = await fetch("/api/users")
const json: ApiResponse<User[]> = await res.json()
if (!json.success) throw new Error(json.error)
return json.data // TypeScript knows this is User[]
}
The satisfies keyword checks the object against the type without widening it. Use it when you want type checking but need to preserve the literal type.
Zod for Runtime Validation
TypeScript types disappear at runtime. Zod validates the shape at the boundary (API inputs, external data) and infers the TypeScript type:
import { z } from "zod"
const CreatePostSchema = z.object({
title: z.string().min(1, "Title required").max(200),
content: z.string().min(10, "Content too short"),
tags: z.array(z.string()).max(5).optional().default([]),
publishAt: z.coerce.date().optional(),
})
// Infer TypeScript type from Zod schema
type CreatePostInput = z.infer<typeof CreatePostSchema>
// In your API route
export async function POST(req: Request) {
const body = await req.json()
const parsed = CreatePostSchema.safeParse(body)
if (!parsed.success) {
return Response.json(
{ error: parsed.error.flatten().fieldErrors },
{ status: 422 }
)
}
// parsed.data is fully typed as CreatePostInput
const post = await db.post.create({ data: parsed.data })
return Response.json(post)
}
One schema, two benefits: runtime validation + compile-time types.
Discriminated Unions for State Machines
AI apps have complex async states. Discriminated unions make them exhaustive:
type MessageState =
| { status: "idle" }
| { status: "loading" }
| { status: "streaming"; partial: string }
| { status: "complete"; content: string; usage: TokenUsage }
| { status: "error"; error: string; retryable: boolean }
function ChatMessage({ state }: { state: MessageState }) {
switch (state.status) {
case "idle":
return null
case "loading":
return <Spinner />
case "streaming":
return <div className="animate-pulse">{state.partial}</div>
case "complete":
return <div>{state.content}</div>
case "error":
return (
<div>
{state.error}
{state.retryable && <button>Retry</button>}
</div>
)
}
}
TypeScript enforces that all cases are handled. Add a new state variant and TypeScript will error everywhere the switch isn't updated.
Generic Repository Pattern
Instead of repeating query logic, use a generic base:
// lib/repository.ts
type FindManyOptions<T> = {
where?: Partial<T>
orderBy?: { [K in keyof T]?: "asc" | "desc" }
take?: number
skip?: number
}
class UserRepository {
async findMany(options?: FindManyOptions<User>): Promise<User[]> {
return db.user.findMany({
where: { deletedAt: null, ...options?.where },
orderBy: options?.orderBy ?? { createdAt: "desc" },
take: options?.take ?? 50,
skip: options?.skip ?? 0,
})
}
async findById(id: string): Promise<User | null> {
return db.user.findUnique({ where: { id, deletedAt: null } })
}
async findByEmail(email: string): Promise<User | null> {
return db.user.findUnique({ where: { email } })
}
}
export const userRepo = new UserRepository()
Centralizes query logic, adds soft-delete filtering automatically, reduces boilerplate in route handlers.
Strict Environment Variables
Don't use process.env.FOO directly -- it's string | undefined everywhere. Validate at startup:
// lib/env.ts
import { z } from "zod"
const EnvSchema = z.object({
DATABASE_URL: z.string().url(),
NEXTAUTH_SECRET: z.string().min(32),
NEXTAUTH_URL: z.string().url(),
ANTHROPIC_API_KEY: z.string().startsWith("sk-ant-"),
STRIPE_SECRET_KEY: z.string().startsWith("sk_"),
STRIPE_WEBHOOK_SECRET: z.string().startsWith("whsec_"),
})
// Throws at startup if any required env var is missing/invalid
export const env = EnvSchema.parse(process.env)
// Now use env instead of process.env
import { env } from "@/lib/env"
const stripe = new Stripe(env.STRIPE_SECRET_KEY) // string, not string | undefined
Missing env vars now fail at startup with a clear error, not at runtime when the code first executes.
Template Literal Types for Route Safety
type ApiRoute =
| `/api/users/${string}`
| `/api/posts/${string}`
| `/api/chat`
| `/api/webhooks/stripe`
async function apiCall(route: ApiRoute, options?: RequestInit) {
return fetch(route, options)
}
apiCall("/api/users/123") // OK
apiCall("/api/chat") // OK
apiCall("/api/wrong-route") // TypeScript error
Catches broken internal API calls at compile time.
Utility Types Worth Knowing
// Make some fields optional
type UpdateUser = Partial<Pick<User, "name" | "bio" | "image">>
// Make all fields required
type RequiredUser = Required<User>
// Extract specific fields
type UserPreview = Pick<User, "id" | "name" | "image">
// Remove specific fields
type PublicUser = Omit<User, "passwordHash" | "stripeCustomerId">
// Make readonly (prevents mutation)
type ImmutableUser = Readonly<User>
// Extract the return type of a function
type DbUser = Awaited<ReturnType<typeof db.user.findUnique>>
Awaited<ReturnType<...>> is particularly useful for getting the TypeScript type of a Prisma query result without duplicating the type definition.
All of these patterns are implemented in the AI SaaS Starter Kit -- Zod schemas, env validation, typed API responses, and discriminated union state management.
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