The If Output Contains node allows you to create dynamic paths in your workflow by checking if LLM outputs contain specific text patterns or match regex expressions.

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Functionality

The If Output Contains node acts as a content filter, analyzing LLM output text and directing the flow based on pattern matching. This can be useful for:
  • Output Validation: Ensure AI outputs meet specific criteria
  • Content Classification: Route outputs based on detected themes or content
  • Quality Control: Identify and handle outputs with particular characteristics
  • Output Filtering: Filter out unwanted content patterns
  • Automated Output Handling: Route different types of outputs to appropriate processing steps

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Node Properties

For each condition you add, you can configure:
  • Text to Match: The pattern or text to search for in the LLM output
  • Use Regex: Toggle to enable regular expression pattern matching
  • Case Sensitive: Toggle to make the pattern matching case sensitive
Multiple conditions are evaluated using OR logic — if any condition is true, the workflow will proceed down the true path. To use AND logic, simply chain multiple If Output Contains nodes together.

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Usage Examples

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Scenario: Output Quality Control

Let’s say you want to handle AI outputs differently based on their content characteristics:
  1. Add an If Output Contains node after your LLM node
  2. Add conditions for quality checks:
    • Text: “I apologize” (to catch uncertainty or inability to answer)
    • Text: “I don’t know” (to identify knowledge gaps)
  3. Connect the “True” path to fallback handling
  4. Connect the “False” path to normal processing

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Scenario: Content Classification with Regex

To categorize outputs based on specific patterns:
  1. Add an If Output Contains node
  2. Enable “Use regex”
  3. Add a condition with pattern: \$\d+(\.\d{2})? (to identify outputs containing dollar amounts)
  4. Route matching outputs to financial processing nodes

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Tips and Best Practices

  • Place this node immediately after LLM nodes to analyze their outputs
  • Use simple text matches for basic content detection
  • Implement regex for more complex pattern recognition
  • Consider case sensitivity based on your content requirements
  • Chain multiple nodes for sophisticated output analysis
  • Test conditions with various LLM outputs to ensure reliable routing
  • Consider checking the Execution Logs to track which conditions are being triggered