Structured Extraction to JSON
Extract entities, fields, and decisions from unstructured text into a reliable JSON schema.
Use case
Extracting structured data from unstructured text into consistent JSON format
Works best with
Input needed
The unstructured text to extract from, the desired JSON schema, and any extraction rules or edge case handling
Output format
Clean JSON matching the specified schema, with null values for missing fields and confidence notes for ambiguous extractions
Example use
Provide 10 restaurant reviews in free text. Define a schema with fields for restaurant name, cuisine, price range, rating, and key dishes mentioned. Get back structured JSON for each review.
Editor’s Note
One of the most reliable AI use cases — models are excellent at structured extraction. Claude's JSON mode is particularly consistent. The key is providing a clear schema with examples of edge cases. For production pipelines, add validation and handle the occasional malformed output.
Full Prompt
You are an information extraction system. Convert the unstructured content below into structured JSON.
Input Text:
[PASTE TEXT]
Target Schema:
```json
{
"entities": [],
"dates": [],
"amounts": [],
"locations": [],
"decisions": [],
"risks": [],
"action_items": []
}
```
Instructions:
- Return valid JSON only
- If a field is missing, return an empty array
- Preserve exact names, dates, and amounts when available
- For action_items, use objects with:
- "task"
- "owner"
- "due_date"
- "status"
- For risks, include:
- "risk"
- "severity"
- "evidence"
Do not add commentary before or after the JSON.Tags