LLM Structured Outputs

Extract structured JSON from unstructured text and validate against a Pydantic schema.

Updated Apr 26, 2026

Search Tags:#structured data #data extraction #LLM #Pydantic #JSON validation

Ready to run

npx skills add https://github.com/Renzo-Tognella/UniversalThingsForMyAgents --skill llm-structured-outputs-renzo-tognella

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: LLM Structured Outputs
Source: https://github.com/Renzo-Tognella/UniversalThingsForMyAgents/tree/main/skills/08_llm_structured_outputs
Command: npx skills add https://github.com/Renzo-Tognella/UniversalThingsForMyAgents --skill llm-structured-outputs-renzo-tognella

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires instructor, pydantic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the problem of extracting structured JSON data from unstructured text using Large Language Models (LLMs) with validation through Pydantic.

Core Features & Use Cases

Structured Data Extraction: Extracts structured data from text using LLMs.
Pydantic Validation: Validates the extracted JSON against a Pydantic schema to ensure data integrity.
Retry Strategies: Implements retry strategies for handling invalid JSON responses.
Use Case: For instance, use this Skill to extract product information from product descriptions on an e-commerce website.

Quick Start

Use the LLM Structured Outputs skill to extract structured data from the text "The latest product model features a 12MP camera and a 6GB RAM."

Frequently Asked Questions about LLM Structured Outputs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract structured JSON from unstructured text using an LLM?▼

You can extract structured JSON from unstructured text by passing the text to an LLM and validating the response against a Pydantic schema to ensure data integrity. This skill uses the instructor library to handle the extraction and validation process.

How do I validate LLM JSON output against a Pydantic schema?▼

Validating LLM JSON output against a Pydantic schema involves defining your expected data model in Pydantic and using the instructor library to enforce it. The skill automatically validates extracted data and implements retry strategies for invalid JSON responses.

What is the best way to extract product information from unstructured descriptions?▼

The best way to extract product information from unstructured descriptions is using LLM structured data extraction with Pydantic validation. This approach ensures extracted attributes like camera specs or RAM are accurately captured and formatted as structured JSON.

Do I need the instructor library to get structured data from LLMs?▼

Yes, you need the instructor library along with Pydantic to use this specific structured data extraction approach. These dependencies are required to validate LLM responses and handle retry strategies for invalid JSON outputs.

How does Pydantic validation handle invalid JSON responses from LLMs?▼

Pydantic validation handles invalid JSON responses by detecting schema mismatches and triggering retry strategies. This ensures the LLM corrects its output to match the defined Pydantic schema before returning the final structured data.

Can I extract structured data from technical documentation using LLMs?▼

Yes, you can extract structured data from technical documentation, customer reviews, or product descriptions using LLMs. The skill validates the extracted JSON against a Pydantic schema to ensure data integrity for various unstructured text sources.