LLM as a typed component, not a chatbot.

JSON schema enforcement, validator loops with bounded retries, repair prompts on parse failure. Zod schemas mirror your API contract. Type-safe end-to-end — model output to client.

TypeScriptZodOpenAIAnthropic

Start this engagement →All LLM services →

Cycle

8 weeks · schema to ship

Stack

TypeScript · Zod · OpenAI / Anthropic

Output

Schemas + validator loop + repair prompts

Discipline

Output validates or doesn't ship — no maybe

[WHY THIS EXISTS]

JSON.parse on model output is a vulnerability.

The model returns 'almost-valid' JSON, your parser throws, your endpoint returns a 500, and the user gets nothing. Or worse — the model returns valid-but-wrong JSON, the field is missing, downstream code crashes with an undefined access. Structured output is about making the gap explicit.

Zod schemas as the single source of truth for both API and prompt
Validator loop — parse, validate, repair, retry — bounded retries
JSON-mode / function-calling on the model when supported
Type-safe end-to-end — TypeScript types derived from the Zod schema

[HOW WE BUILD IT]

Schema first, prompt second.

Zod as the contract

Define the output shape as a Zod schema. Generate TypeScript types from it. Pass the JSON schema to the model. One source of truth across API, prompt, and client.

Validator loop

Parse → validate → if invalid, send a repair prompt with the specific error → retry up to N times → fail loud if budget exhausts. No silent corrupted output.