Structured Output Designer

Design reliable structured data extraction and generation systems powered by LLMs, with JSON Schema design, provider-specific configuration, validation pipelines, and production error handling.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

85.05

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.8

Completeness (10%)

8.5

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.5

Best for

▸Design JSON Schema for LLM-powered data extraction from customer support tickets
▸Configure OpenAI structured outputs for invoice processing with validation pipelines
▸Build reliable product catalog extraction from unstructured vendor PDFs
▸Implement multi-provider structured output failover for form data generation
▸Create schema-validated lead scoring from sales conversation transcripts

What you'll get

●Complete JSON Schema with field descriptions, validation constraints, and provider-specific adaptations for OpenAI/Claude compatibility
●Production-ready Python code with Pydantic models, error handling, retry logic, and graceful degradation patterns
●Monitoring dashboard configuration tracking schema compliance rates, validation failures, and provider performance metrics

Not designed for ↓

×General prompt engineering without structured output requirements
×Building complete LLM applications beyond the structured output component
×Training or fine-tuning models for better structured output performance
×Frontend UI development for displaying structured data

Expects

Clear requirements for input data type, desired output structure, validation constraints, and acceptable failure rates for structured LLM outputs.

Returns

Complete structured output pipeline including JSON Schema, provider configuration, validation code, error handling patterns, and monitoring setup.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

structured-outputjson-schemallm-integrationdata-extractionprompt-engineeringvalidationpydanticzodconstrained-decodingopenai-apiclaude-apischema-designerror-handlingproduction-ml

Research Foundation: 8 sources (4 official docs, 1 academic, 2 industry frameworks, 1 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Works well with

API Design ArchitectGold Data Quality EngineerGold Error Handling & Resilience EngineerGold LLM Evaluation Framework DesignerGold Prompt Engineering StrategistGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

LLM Fine-Tuning StrategistGold Prompt Chain ArchitectGold LLM Observability EngineerGold

Common Workflows

LLM Data Pipeline Development

Design structured output schemas, implement validation pipelines, then set up production monitoring for LLM-powered data extraction systems

structured-output-designer→Data Quality Engineer→LLM Observability Engineer

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