JSON Prompt
Generator.
Free builder for prompts that return strict, parseable JSON. Schema, null policy, and verification formats for every major LLM.
Machine-readable output, zero parse errors, three strictness levels.
Describe what you want
3 prompt variations
Click Copy to useTask: [describe the task]
Return ONLY valid JSON matching this schema:
```json
{
"field_1": "string",
"field_2": "number"
}
```
Missing values policy: use null for missing values.
Do not include any text before or after the JSON. No markdown fences, no commentary.# TASK
[describe the task]
# OUTPUT SCHEMA
```json
{
"field_1": "string",
"field_2": "number"
}
```
# RULES
- Strictness: strict (schema must match exactly).
- Missing values: use null for missing values.
- Escape all string quotes correctly.
- Do not wrap the JSON in markdown fences in the final response.
# OUTPUT
Return only the JSON object. No preamble, no closing remarks.
(Pair with response_format: { type: "json_object" } in the API call.)<task>
[describe the task]
</task>
<output_schema>
{
"field_1": "string",
"field_2": "number"
}
</output_schema>
<rules>
1. The response MUST be a single JSON object matching the schema exactly.
2. Include all required fields. Use null for missing values.
3. Do not include ANY non-JSON text. No "Here is the result:" preamble.
4. If the input is ambiguous, still produce valid JSON and use the missing value policy for uncertain fields.
</rules>
<verification>
Before responding, mentally validate: does this parse with JSON.parse? Does every required field exist? Are types correct?
</verification>
<deliver>
Return only the JSON. Nothing else.
</deliver>Under the hood
Why JSON output beats prose for structured data.
JSON output feeds directly into databases, dashboards, and downstream tools. No regex parsing, no fragile string splits. If the schema is right, the data flows.
GPT-5 response_format, Claude tool-use, and Gemini response_mime_type enforce JSON at decode time. Parse error rates drop from a few percent to near zero.
A schema is testable. You can validate every response against it and reject malformed output before it enters your system. Prose output has no equivalent check.
Related free tools
Specialized generators for specific tasks.
FAQ
Questions about JSON output prompting.
What is a JSON prompt?+
A JSON prompt tells the model to return structured output in JSON format instead of prose. You specify the schema, the missing value policy, and any validation rules. Output can be parsed directly with JSON.parse in your application code. Used for extraction, classification, agent tool calls, and any flow that needs machine-readable output.
Which models support strict JSON output?+
GPT-4o and GPT-5 support the response_format parameter with type: 'json_object' or type: 'json_schema'. Claude supports JSON via tool-use mode or by instructing the model to start its reply with an open brace. Gemini 2.5 supports response_mime_type: 'application/json' with a response_schema field. DeepSeek-R1 handles JSON well via prompt instruction without a dedicated mode.
Should I use JSON mode or just prompt for JSON?+
Use the dedicated mode when available. GPT's response_format and Gemini's response_mime_type enforce the format at decode time, so parse errors drop to near zero. Prompt-only JSON works but allows occasional stray text, unclosed braces, or prose preamble. If you are building a production flow, use the mode. If you are prototyping, prompt-only is fine.
What is the difference between strict and tolerant schemas?+
Strict schemas require the output to match exactly: every required field present, no extra fields, correct types. Tolerant schemas allow the model to include extra fields or omit optional ones. Strict is better for downstream parsers that break on unexpected keys. Tolerant is better for exploration where the model might surface useful fields you did not anticipate.
What should I do about missing values?+
Four common policies: use null, omit the field entirely, use an empty string or empty array, or use a sentinel like 'unknown'. Null is the most common and works with most parsers. Omit is cleaner for optional fields but breaks schemas that require the field. Sentinels are useful when you want to distinguish 'not found' from 'not applicable'. Pick one and apply it consistently.
How do I handle nested objects and arrays?+
Specify the structure in the schema and give an example. Models handle two or three levels of nesting reliably. For deeper nesting, flatten where possible or split into multiple calls. Arrays with a fixed structure (like a list of extracted entities) work better than free-form lists. Always include an example in the prompt for nested cases.
Why does the model sometimes include markdown fences around JSON?+
Training data includes many code blocks, so models default to wrapping JSON in triple-backtick fences. Solve this explicitly: include 'do not wrap the JSON in markdown fences' in the rules, and if the model still does it, strip fences with a regex before parsing. GPT's response_format eliminates this problem entirely.
When is JSON output the wrong choice?+
When the task is inherently prose: creative writing, conversational responses, long-form analysis. Forcing JSON on those tasks produces stilted output. JSON shines when the output feeds a downstream system: databases, dashboards, tool calls, or data pipelines. If a human is the direct consumer of the output, plain text is usually better.