Vague prompts produce vague JSON. This free unlimited json prompt generator turns your spec into a strict contract — full JSON schema, type constraints, enum sets, refusal-on-malformed and a few-shot validation example. Drop it into ChatGPT, Claude, Gemini or any OpenAI-compatible API; the json prompt generator output stays parseable on the first try.
You'll need an account to optimize. .
Before vs After
Most 'output JSON' prompts get broken JSON. This json prompt generator emits a strict contract LLMs respect because every field is enumerated.
“Make the LLM output JSON describing a product.”
# Role
You are a strict JSON-generating API. You output only valid JSON. No prose, no markdown, no preamble.
# Task
Extract structured product information from the user's free-text description.
# Schema (strict — extra fields prohibited)
{
"name": "string (required, 1-120 chars)",
"category": "string (required, one of: electronics | clothing | home | beauty | grocery | other)",
"price_usd": "number (required, > 0, max 100000, two decimals)",
"currency": "string (required, ISO 4217, default 'USD')",
"in_stock": "boolean (required)",
"stock_qty": "integer | null (optional; only present if in_stock=true)",
"tags": "array<string> (required; 0-20 unique items, each 1-32 chars, lowercase, kebab-case)",
"description_short": "string (required, 1-280 chars)",
"notes": "string | null (optional; max 1000 chars)"
}
# Rules
- Return ONLY a single JSON object matching the schema above.
- No trailing commas.
- All strings must be UTF-8.
- If a required field can't be inferred, return exactly: {"error": "MISSING_FIELD", "field": "<field_name>"}
- If the user input is gibberish or empty, return exactly: {"error": "UNPARSEABLE_INPUT"}
- Never wrap the JSON in markdown fences. Never add explanatory text.
# Few-shot example
User: "Black leather jacket, large, $189.99, 4 in stock at the warehouse, tags: leather, jacket, motorcycle"
Assistant:
{
"name": "Black leather jacket",
"category": "clothing",
"price_usd": 189.99,
"currency": "USD",
"in_stock": true,
"stock_qty": 4,
"tags": ["leather", "jacket", "motorcycle"],
"description_short": "Black leather jacket, large size, available in stock.",
"notes": null
}How it works
Four steps from 'output some JSON' to a json prompt generator contract any LLM respects on the first call.
The json prompt generator opens with 'You are a strict JSON-generating API'. Anchoring the LLM as an API removes the prose habit.
Every field gets a type, constraints and required/optional. The json prompt generator emits a schema strict enough that extra fields are rejected.
Malformed input → structured error. The json prompt generator names the error shape ({error, field}) so downstream code can branch cleanly.
One worked example. The json prompt generator includes it because it cuts malformed-output rate by ~40% in practice.
Every output is a strict contract — types, constraints, enum sets, refusal behaviour, few-shot anchor.
A 'You are a strict JSON-generating API' role line that suppresses the LLM's prose habit.
Full schema with types, length limits, enum sets and required-vs-optional marked.
Refusal contract — what the LLM should output if a field is missing or input is gibberish.
A few-shot example that anchors format and reduces malformed-output rate in production.
Explicit 'no markdown fences, no preamble' clause — most json prompt generators forget this and pay for it.
Output adapter notes for OpenAI structured output / Claude JSON mode / Gemini schema mode where supported.
Anywhere downstream code has to parse LLM output and can't afford a try/except over every call.
REST endpoints that call an LLM and return JSON to the client. The json prompt generator hardens the contract so a malformed call is a 4xx, not a 500.
Function-calling APIs require strict schemas. The json prompt generator emits OpenAI / Claude / Gemini-compatible schemas with the right field shapes.
Free-text into structured rows. The json prompt generator pins types so the CSV / DB column never sees a string where it expects an integer.
Extract structured form data from a user's free-text. The json prompt generator handles the messy 'they wrote it wrong' edge case via the refusal contract.
Judge prompts need parseable verdicts. The json prompt generator emits enum-bound 'verdict' fields so eval pipelines stay deterministic.
Stage N's JSON is stage N+1's input. The json prompt generator pins every field so each stage gets exactly what the next stage expects.
Same json prompt generator brief, different paste target. The output adapts per model's native structured-output mode.
Native JSON mode + structured outputs. The json prompt generator emits a brief that drops into response_format={'type':'json_schema'}.
Claude's tool-use respects strict JSON. The json prompt generator emits XML-friendly section headers for Claude's preferred format.
Gemini's responseSchema field accepts the same schema directly. The json prompt generator output drops in as-is.
Cost-effective at scale. The json prompt generator emits compact schemas so prompt tokens stay low.
Structured-output support varies. The json prompt generator emits explicit refusal contracts so local models still produce parseable JSON or a clear error.
Constrained-generation frameworks pair with the json prompt generator's schema directly — paste in, get strict generation.
Questions
Yes — ten free credits per day per account. A json prompt generator run costs about 5 credits, so the free tier supports a few JSON schemas a day, forever.
Yes. The json prompt generator emits a schema you can drop directly into OpenAI's response_format={'type':'json_schema'}. Claude's tool-use and Gemini's responseSchema accept the same shape.
The json prompt generator emits a refusal contract — the LLM should return a structured {error, field} object when it can't comply. If you still see malformed output, the few-shot anchor section is what tightens it; add another worked example.
Yes. Mention the nesting in the spec and the json prompt generator emits a schema with full type recursion (arrays of objects with nested fields). Works for any reasonable depth.
The json prompt generator handles the prompt-engineering layer — role, refusal behaviour, few-shot anchor, no-markdown clause, OpenAI/Claude/Gemini adapters. The schema itself is half the work; the framing is the other half.
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