TypeScript Code Generation from Figma Designs
CodeFlow Lab generates strict TypeScript — fully typed React components, Drizzle ORM schemas, and Zod validators — from a single intermediate representation that guarantees cross-file type consistency.
Why Cross-File Type Safety Is Hard for AI Generators
Writing valid TypeScript in a single file is something any language model can do. The hard problem is cross-file type consistency: the User type that the useUsers hook returns must exactly match the shape that the UserCard component expects. The createUser mutation must accept exactly the fields that the Drizzle schema declares as required. The route params the router declares must match what useRoute returns in the page component.
When each file is generated from a language model without a shared type model, these contracts are invented independently — and they diverge. You end up with TypeScript that compiles file-by-file but fails the moment you run tsc --noEmit across the project.
CodeFlow Lab solves this by declaring every type in the IR before generating any file. Generators don't invent types; they consume declarations. The same User entity definition that backs the Drizzle schema backs the React Query hook and the component props — derived once, used everywhere.
What TypeScript CodeFlow Lab Generates
Typed React Components
- Interface-typed props (never 'any')
- Typed children via React.ReactNode
- Typed ref forwarding where applicable
- Generic components with constrained type params
Full-Stack Type Safety
- Drizzle ORM table with $inferSelect / $inferInsert
- Zod insert schemas for API validation
- Typed React Query hooks (useQuery, useMutation)
- Typed route params via wouter useRoute
Strict TypeScript Config
- strict: true in tsconfig.json
- noUncheckedIndexedAccess enabled
- Path aliases (@/ for src) configured
- Module resolution: bundler (Vite-compatible)
Quality Verified
- ts.createProgram run before delivery
- Error count reported in quality score
- TypeScript ratio > 80% required for 'complete' status
- any usage flagged in generation report
Frequently Asked Questions
Can AI generate production-quality TypeScript?
Yes, with the right architecture. The challenge is not whether AI can write valid TypeScript syntax — it can. The challenge is coherence: ensuring that the TypeScript types in one file match the data shapes used in another, that generics are propagated correctly, and that type narrowing is consistent across the entire project. Tools that generate TypeScript from an intermediate representation (IR) with a declared type contract for every entity can deliver zero-error TypeScript at scale. Tools that generate file-by-file from a language model cannot reliably guarantee this.
How does CodeFlow Lab ensure type safety in generated code?
CodeFlow Lab declares a type contract for every entity, prop, and data binding in its IR before generating any file. Code generators consume these contracts and are constrained to emit TypeScript consistent with them. When a component needs a user object, the IR already declares the User type — the generator doesn't invent one. The result is that type errors are caught at the IR validation stage, not after you open the generated files in your IDE.
What TypeScript features does CodeFlow generate?
CodeFlow Lab generates: strict TypeScript (strict: true in tsconfig), typed React component props using interfaces, Drizzle ORM table definitions with inferred types, Zod schemas for API request validation, React Query hooks with typed return values, typed route params and search params, discriminated unions for state modeling, and utility types (Partial, Pick, Omit) where appropriate. Generated code targets TypeScript 5.x with all strict checks enabled.
How does CodeFlow handle TypeScript across the full stack?
The same IR that defines entities on the frontend defines the database schema and API types on the backend. A User entity declared in the IR generates: a Drizzle ORM table schema, a Zod insert schema for API validation, a TypeScript type for the select shape, and typed React Query hooks for the frontend. The frontend User type and the backend User type are derived from the same IR entity — they cannot diverge.
What TypeScript quality score should I aim for?
CodeFlow Lab scores TypeScript quality on a 0–10 scale. A score of 8+ indicates fewer than 20 TypeScript errors per 100 files, full prop typing, and consistent type usage across the stack. Scores below 6 indicate structural issues (missing types, widespread use of 'any', inconsistent data shapes) that will require manual cleanup before the code is production-ready. Most CodeFlow Lab generations score 9–10 on this scale.
Can I generate TypeScript for an existing codebase?
Yes. CodeFlow Lab's Figma-to-code pipeline can generate TypeScript components that follow your existing type conventions. If your project already has a shared types file or a Drizzle schema, you can provide it as context and CodeFlow Lab will align generated types with your existing definitions rather than creating new ones. This avoids type duplication and makes the generated components drop-in compatible with your codebase.