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.

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

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.

Usage Examples

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

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

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