AI System Prompt to Force Strict JSON-Only Output From Any Model
This system prompt is built for anyone wiring an LLM into a pipeline, API, or app where downstream code parses the model's reply automatically. It forces ChatGPT, Claude, or Gemini to return a single, valid JSON object with a fixed schema and nothing else — no greeting, no markdown fences, no "Here is your JSON:" preamble that breaks a JSON.parse() call in production.
The core trick is combining a hard output contract ("respond with raw JSON only") with explicit handling for edge cases: what the model should do when a required field has no value, when the input is ambiguous, or when it can't confidently answer at all. Without that guidance, models tend to either hallucinate a plausible-looking value or wrap the JSON in explanatory text, both of which silently break a parser.
If you're assembling this system prompt by hand and want to double-check the structure before shipping it, running it through a tool like Prompt Formatter can catch inconsistent key naming or malformed schema blocks before they reach production.
Prompt template
ROLE: You are a strict data-extraction and formatting engine. You do not converse, explain, or add commentary.
TASK: Given the input below, extract the requested information and return it as a single JSON object matching the schema exactly.
SCHEMA: { "[FIELD_NAME_1]": "[TYPE, e.g. string]", "[FIELD_NAME_2]": "[TYPE, e.g. number or null]", "[FIELD_NAME_3]": "[TYPE, e.g. array of strings]" }
RULES:
- Output ONLY the JSON object. No greeting, no explanation, no markdown code fences, no trailing text.
- If a field's value cannot be determined from the input, set it to null. Never guess or fabricate a value.
- Preserve the exact key names and casing shown in the schema.
- If the input is empty or unreadable, return: {"error": "unparseable_input"}
INPUT: [PASTE_RAW_INPUT_TEXT_HERE]
Example input
ROLE: You are a strict data-extraction and formatting engine. You do not converse, explain, or add commentary.
TASK: Given the input below, extract the requested information and return it as a single JSON object matching the schema exactly.
SCHEMA: { "customer_name": "string", "order_id": "string or null", "issue_category": "string, one of: shipping, billing, product_defect, other", "urgent": "boolean" }
RULES:
- Output ONLY the JSON object. No greeting, no explanation, no markdown code fences, no trailing text.
- If a field's value cannot be determined from the input, set it to null. Never guess or fabricate a value.
- Preserve the exact key names and casing shown in the schema.
- If the input is empty or unreadable, return: {"error": "unparseable_input"}
INPUT: Hi, this is Maria Chen. My package for order #48213 arrived with a cracked screen and I need this fixed today, I have a work call that depends on this laptop.
Example output
{ "customer_name": "Maria Chen", "order_id": "48213", "issue_category": "product_defect", "urgent": true }
When to use it
- Building an API endpoint where an LLM response is parsed directly by backend code
- Chaining multiple model calls where one model's output feeds the next step's input
- Extracting structured fields (names, dates, categories) from free-text user input
- Building a tool-calling or function-calling layer that needs a predictable response shape
Best practices
- Define every field's exact key name, type, and allowed values in the schema so the model has no room to improvise
- Tell the model explicitly what to put in a field when the input doesn't contain that information, such as
nullinstead of guessing - Add a one-line instruction banning markdown code fences, since some models wrap JSON in ```json blocks by default
- Test the prompt against malformed or incomplete input, not just clean examples, to see how it fails before you ship it — running it through Prompt Debugger is a fast way to surface those edge cases
Common mistakes
- Asking for JSON but never specifying required vs optional fields, so the model omits fields inconsistently
- Forgetting to ban commentary, which lets the model prepend "Sure, here's the JSON:" and break strict parsers
- Not defining a fallback value for missing data, causing the model to invent plausible-sounding but false values
- Testing only with ideal inputs and never checking how the schema holds up against messy or incomplete ones
FAQs
Why does ChatGPT sometimes wrap JSON in markdown code fences even when I ask for raw JSON?
Models are trained on a lot of markdown-formatted text, so without an explicit instruction banning code fences, they default to the formatting pattern they've seen most often for code-like output. Adding a direct line such as "no markdown code fences" in the system prompt usually fixes this.
How do I stop the model from inventing values for fields it can't find in the input?
Define a fallback explicitly, such as "set to null if not present," rather than leaving the behavior undefined. Models tend to fill gaps with plausible-sounding guesses unless they're told that a missing value is an acceptable, expected outcome.
Does this JSON-only system prompt work the same way on Claude, ChatGPT, and Gemini?
The core structure works across all three, though Claude and Gemini are generally more likely to follow a strict "no commentary" instruction on the first try, while some ChatGPT models occasionally need the rule restated if the input is unusual. Testing against your specific model and prompt version is still worth doing.
What should the schema look like for nested or array-based data?
Spell out the nested structure in the schema block itself, showing an example array entry or sub-object, rather than describing it only in prose. Models follow a literal schema shown as JSON far more reliably than a verbal description of the same structure.
Which Cuelara tool can help me find weaknesses in this prompt before I put it into production?
Prompt Debugger — it scans a prompt like this one for vague constraints and missing edge cases, such as what happens on empty input or ambiguous fields, before they cause a parsing failure downstream.