LLM Boolean Evaluator

The LLM Boolean Evaluator is a specialized component that evaluates text responses from language models and returns boolean (true/false) results based on specific criteria or conditions. It's particularly useful for validation checks and conditional logic in LLM workflows.

LLM Boolean Evaluator interface and configuration

Usage Note: Ensure that your evaluation criteria are clearly defined and unambiguous. The evaluator works best with well-defined conditions that can be definitively evaluated as true or false.

Component Inputs

Input Text: The text to be evaluated
Example: "The statement to be verified"
Generated Output: The model's response to evaluate
Example: "Response to check against criteria"
Context(s): Additional context for evaluation
Example: "Relevant contextual information"
LLM Model: The language model to use for evaluation
Example: "gpt-4", "claude-2"
Evaluation Prompt: The specific condition to evaluate
Example: "Does this response contain a valid solution?"

Component Outputs

Boolean Result: True/False evaluation result
Example: true or false
Confidence Score: Confidence in the evaluation
Example: 0.95 (95% confidence)
Explanation: Reasoning for the boolean result
Detailed explanation of the evaluation decision

How It Works

The LLM Boolean Evaluator processes inputs through a systematic evaluation pipeline to produce reliable true/false determinations. It uses context-aware analysis and specific evaluation criteria to make its decisions.

Evaluation Process

Input and context analysis
Criteria evaluation
Boolean determination
Confidence calculation
Explanation generation
Result validation

Use Cases

Validation Checks: Verify if responses meet specific criteria
Quality Control: Check if responses maintain quality standards
Compliance Verification: Ensure responses follow guidelines
Decision Making: Support automated decision processes
Content Filtering: Determine if content meets specific conditions

Implementation Example

const booleanEvaluator = new LLMBooleanEvaluator({
  inputText: "Is this solution secure?",
  generatedOutput: "The solution implements encryption...",
  context: "Security evaluation context",
  llmModel: "gpt-4",
  evaluationPrompt: "Does this response provide a secure solution?"
});

const result = await booleanEvaluator.evaluate();

// Output:
// {
//   result: true,
//   confidence: 0.92,
//   explanation: "The solution implements proper security measures..."
// }

Additional Resources

Best Practices

Define clear evaluation criteria
Provide comprehensive context
Set appropriate confidence thresholds
Validate results with test cases
Monitor evaluation accuracy

Documentation