LLM Category Evaluator

The LLM Category Evaluator is a specialized component that evaluates and categorizes text responses from language models based on predefined categories and criteria. It helps ensure responses align with expected categories and maintains quality control in LLM outputs.

LLM Category Evaluator interface and configuration

Usage Note: Ensure that your categories are well-defined and mutually exclusive for optimal evaluation results. The evaluator requires clear category definitions to provide accurate assessments.

Component Inputs

Input Text: The text to be evaluated
Example: "The response to be categorized"
Generated Output: The model's generated text
Example: "Model's response for categorization"
Context(s): Additional context for evaluation
Example: "Relevant context information"
LLM Model: The language model to use for evaluation
Example: "gpt-4", "claude-2"
Categories: List of possible categories
Example: ["technical", "non-technical", "unclear"]

Component Outputs

Category Result: The determined category
Example: "technical"
Confidence Score: Confidence in the categorization
Example: 0.95 (95% confidence)
Explanation: Reasoning for the categorization
Detailed explanation of why the category was chosen

How It Works

The LLM Category Evaluator uses a sophisticated evaluation process to analyze and categorize text. It leverages the specified LLM to understand context, analyze content, and make informed category assignments.

Evaluation Process

Input text and context analysis
Category criteria matching
Content evaluation against each category
Confidence score calculation
Category assignment
Explanation generation

Use Cases

Content Classification: Categorize responses by type or domain
Quality Assurance: Verify response categories match expectations
Response Validation: Ensure responses fit within desired categories
Automated Sorting: Categorize large volumes of responses
Content Filtering: Filter responses based on categories

Implementation Example

const categoryEvaluator = new LLMCategoryEvaluator({
  inputText: "How do I configure a REST API endpoint?",
  generatedOutput: "To configure a REST API endpoint...",
  context: "Technical documentation context",
  llmModel: "gpt-4",
  categories: ["technical", "non-technical", "unclear"]
});

const result = await categoryEvaluator.evaluate();

// Output:
// {
//   category: "technical",
//   confidence: 0.95,
//   explanation: "The response discusses technical API configuration..."
// }

Additional Resources

Best Practices

Define clear and distinct categories
Provide comprehensive context when available
Use appropriate LLM models for your use case
Regularly validate category definitions
Monitor and adjust confidence thresholds

Documentation