LLM Structured Output
Generate JSON following a JSON Schema using an LLM
Node Type
Action
Category
AI & Language
Icon
Brain
Overview
The LLM Structured Output node generates structured JSON responses from Large Language Models using a provided JSON Schema. This powerful AI integration ensures consistent, validated output that can be used as input for downstream nodes, making it perfect for creating structured data, API responses, or any scenario requiring consistent JSON output.
Key Features
- • Schema-Driven Output: Uses JSON Schema to enforce consistent output structure
- • Automatic Validation: Validates LLM responses against the provided schema
- • Zod Integration: Converts JSON Schema to Zod for runtime validation
- • PDF Support: Include PDF files for multimodal processing
- • System Prompts: Optional system-level instructions for better control
- • Cost Tracking: Monitor API usage costs for budget management
- • Structured Data: Perfect for generating consistent data for downstream nodes
Prerequisites
AI Service Access
Must have access to Large Language Model services
JSON Schema Knowledge
Understanding of JSON Schema structure
Content Requirements
Prompt and system prompt design
Technical Requirements
System capabilities needed
Node Configuration
Required Fields
Prompt
The main user prompt sent to the LLM. This should clearly describe what structured data you want the AI to generate, following the provided JSON schema.
Schema Fields
JSON Schema object that defines and enforces the output structure. The LLM will generate JSON that conforms to this schema. Use the JSON Schema standard format.
Optional Fields
Model
Which LLM model to use for generation. If not specified, uses the default model.
System Prompt
Optional system prompt to steer the LLM's behavior and provide context for structured output generation.
Optional PDF files to include with the prompt for multimodal processing. The LLM can analyze both text and visual content from the PDF.
Best Practices
Do's
- • Design clear, well-structured JSON schemas for consistent output
- • Use descriptive field names and types in your schema
- • Provide clear prompts that specify the desired output format
- • Include examples in your prompts when helpful for complex schemas
- • Use system prompts to provide context and behavior guidelines
- • Test your schema with sample prompts before production use
- • Monitor API costs and optimize prompts for efficiency
- • Use appropriate model selection based on complexity requirements
Don'ts
- • Don't use overly complex schemas that may confuse the LLM
- • Avoid ambiguous field names or unclear schema definitions
- • Don't forget to validate your JSON schema before use
- • Avoid prompts that don't clearly specify the output format
- • Don't ignore schema validation errors - fix them promptly
- • Avoid overly restrictive schemas that limit useful output
- • Don't assume the LLM will understand complex schema relationships without context
Troubleshooting
Common Issues
Invalid JSON Schema
Symptoms: Node fails with schema validation errors
Solution: Verify that your JSON schema follows the JSON Schema standard. Use online JSON schema validators to check your schema before using it in the node.
Schema Validation Failures
Symptoms: LLM output doesn't match the provided schema
Solution: Improve your prompt to be more specific about the expected output format. Include examples in your prompt and ensure the schema is not overly restrictive.
Poor Output Quality
Symptoms: Generated JSON is irrelevant or doesn't follow the schema
Solution: Enhance your prompt with clear instructions, provide context, and consider using system prompts to guide the LLM's behavior. Test different prompt variations.
PDF Processing Issues
Symptoms: PDF files are not processed correctly
Solution: Ensure PDF files are properly uploaded and accessible. Check that the file format is supported and the content is readable by the LLM.
High API Costs
Symptoms: Unexpectedly high costs from LLM API usage
Solution: Monitor the cost output from the node, optimize prompts to be more concise, and consider using more efficient models for simpler tasks.