Define structured JSON outputs for consistently formatted, parseable LLM responses
Output schemas are JSON Schema definitions that let you define exactly what structure you want from your LLM. Instead of parsing free-form text, you specify the fields, types, and format upfront.
Train your LoRA adapters to follow these schemas for best results, making outputs predictable and ready for downstream processing.
The customer's name is John Smith and you can reach him at [email protected] or call (555) 123-4567. He works at Acme Corp as a Senior Engineer.
{ "name": "John Smith", "email": "[email protected]", "phone": "5551234567", "company": "Acme Corp", "job_title": "Senior Engineer" }
Reduce parsing errors by training models to follow your defined JSON structure consistently.
Define types, enums, and validation rules to guide your models toward more structured, predictable outputs.
Skip regex parsing and error handling. Deserialize directly into your application's data structures.
Define once, use across multiple adapters. Build a library of validated output formats.
Here's a schema for extracting contact information from text
{ "type": "object", "properties": { "name": { "type": "string", "description": "Full name of the person" }, "email": { "type": "string", "format": "email" }, "phone": { "type": "string", "pattern": "^[0-9]{10}$" }, "company": { "type": "string" }, "job_title": { "type": "string" } }, "required": ["name", "email"] }
Train your adapter with this schema to consistently extract contact data in this structured format.
Transform unstructured text into structured JSON with defined fields, making data queryable and ready for processing.
Categorize text into predefined buckets with confidence scores and reasoning chains.
Identify people, places, organizations, and dates in a consistent, structured format.
Generate reports, summaries, or insights in consistent, structured JSON formats.
Create reusable output schemas and pair them with your LoRA adapters