Structured output prompting: how to get reliable JSON, tables, and lists from any AI

Techniques and templates for prompting AI to return structured, machine-readable output consistently — without hallucinated fields, format drift, or plain-text contamination.

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Why structured output prompts fail

No schema: asking for 'JSON with the extracted data' without specifying exact field names, types, and nesting almost always produces inconsistent results across runs.
Prose leakage: the model adds an explanation before or after the JSON block. This breaks parsers. Fix: 'Return only the JSON object. No explanation, no markdown code fences, no other text.'
Hallucinated fields: the model invents field names it thinks you want. Fix: provide the exact schema — field names, their types, and what to put when a value is not found ('null', empty string, or a sentinel like 'N/A').
Nested structure mismatch: you ask for a list but get a dict, or vice versa. Fix: show an example output in the prompt — not just a description, but the actual structure with placeholder values.
Inconsistent date/number formats: the model formats dates differently across responses. Fix: specify the exact format ('ISO 8601: YYYY-MM-DD') and give an example value.

The four elements of a reliable structured output prompt

1. Task description: what to extract or generate, and from what input. 'Extract all line items from the invoice text below.'
2. Exact schema: the complete JSON structure with field names, types, and instructions for missing values. Paste the schema literally, not as a prose description.
3. Example output: a complete, filled-in example with realistic placeholder values. Models learn format from examples faster than from descriptions.
4. Output constraint: 'Return only the JSON. No commentary, no markdown formatting, no preamble.' Put this at the end of the prompt — it is the last instruction the model sees before generating.

Templates

Structured data extraction to JSON

Extract structured data from the text below and return it as JSON.

Text to process:
"""
[Paste the input text here — an invoice, email, form, article, etc.]
"""

Return a JSON object matching this exact schema:
{
  "field_one": string,         // [describe what this field contains]
  "field_two": string | null,  // [describe; null if not found]
  "field_three": number | null, // [describe; null if not found]
  "items": [
    {
      "item_field_a": string,
      "item_field_b": number | null
    }
  ]
}

Rules:
- Use null (not "N/A", not empty string) for any field not found in the text
- Dates in ISO 8601 format: YYYY-MM-DD
- Numbers as numeric types, not strings
- Do not add fields not in the schema
- Return only the JSON object — no explanation, no markdown code fence, no other text

Example output (for reference only — use values from the actual text):
{
  "field_one": "Example value",
  "field_two": null,
  "field_three": 42,
  "items": [
    { "item_field_a": "Item A", "item_field_b": 19.99 }
  ]
}

Opens on home with the prompt prefilled.

Open in Promptea

Structured comparison table

Generate a structured comparison of the items listed below and return the result as a JSON array.

Items to compare:
1. [Item A]
2. [Item B]
3. [Item C — add more as needed]

Dimensions to compare across (evaluate each item on each dimension):
- [Dimension 1, e.g. "Ease of setup"]
- [Dimension 2, e.g. "Cost"]
- [Dimension 3, e.g. "Scalability"]
- [Add or remove dimensions as needed]

Return a JSON array in this exact format:
[
  {
    "item": string,
    "scores": {
      "dimension_1": { "rating": "high" | "medium" | "low", "note": string },
      "dimension_2": { "rating": "high" | "medium" | "low", "note": string },
      "dimension_3": { "rating": "high" | "medium" | "low", "note": string }
    },
    "best_for": string,
    "avoid_if": string
  }
]

Rules:
- Rating must be exactly "high", "medium", or "low" — no other values
- Note: one sentence, under 20 words, factual, no superlatives
- best_for: one sentence describing the ideal use case
- avoid_if: one sentence describing when NOT to choose this item
- Return only the JSON array — no preamble, no explanation, no markdown

Opens on home with the prompt prefilled.

Open in Promptea

FAQ

Which model is best for structured output?

GPT models (especially GPT-4 class and above) have reliable JSON mode and tool-use that produces schema-constrained output with near-zero format failures. Claude is also strong for structured tasks when the schema is explicit in the prompt — its instruction-following is precise. For critical production pipelines, use the model's native JSON mode or function-calling API rather than relying on the prompt alone: it enforces the schema at the generation level, not just as a request.

What should I do when the model keeps adding prose before the JSON?

Two changes fix this in most cases. First, move the output constraint to the very end of your prompt: 'Return only the JSON. Nothing before it, nothing after it.' Second, start your user message with what you want the model to output: some models treat the first token of the response as completion of whatever you started. If you still get prose, switch to the model's native JSON mode if available — it enforces the constraint at the sampling level, making prose contamination structurally impossible.