Query Resolution Evaluator

The Query Resolution Evaluator is a specialized component that assesses how effectively queries are resolved in conversations. It analyzes the completeness, accuracy, and relevance of responses to user queries across conversation entries.

Query Resolution Evaluator interface and configuration

Usage Note: Provide complete conversation context for accurate evaluation. The evaluator's effectiveness depends on having sufficient context about the query and its resolution process.

Component Inputs

Conversation Entries: The conversation history to evaluate
Example: Array of conversation messages
LLM Model: The language model for evaluation
Example: "gpt-4", "claude-2"
Max Tokens: Maximum tokens for evaluation
Example: 2000

Component Outputs

Resolution Score: Overall query resolution effectiveness
Example: 0.95 (95% effective)
Resolution Details: Specific aspects of query resolution
Detailed breakdown of resolution quality
Improvement Suggestions: Areas for better resolution
Recommendations for enhancement
Unresolved Points: Aspects of query not fully addressed
List of pending or unclear points

How It Works

The Query Resolution Evaluator analyzes conversation flow and response quality to determine how effectively queries are being addressed. It considers factors like completeness, accuracy, and clarity of responses.

Evaluation Process

Conversation analysis
Query identification
Response evaluation
Resolution assessment
Gap identification
Recommendation generation

Use Cases

Support Quality: Evaluate support response effectiveness
Conversation Analysis: Assess query handling quality
Training Improvement: Identify areas for model enhancement
User Satisfaction: Monitor query resolution success
Process Optimization: Improve query handling workflows

Implementation Example

const queryResolutionEvaluator = new QueryResolutionEvaluator({
  conversationEntries: [
    { role: "user", content: "How do I reset my password?" },
    { role: "assistant", content: "Here are the steps..." }
  ],
  llmModel: "gpt-4",
  maxTokens: 2000
});

const result = await queryResolutionEvaluator.evaluate();

// Output:
// {
//   resolutionScore: 0.92,
//   details: {
//     completeness: 0.95,
//     clarity: 0.90,
//     accuracy: 0.92
//   },
//   suggestions: ["Add verification step explanation"],
//   unresolvedPoints: ["Account recovery options"]
// }

Additional Resources

Best Practices

Provide complete conversation context
Set appropriate evaluation thresholds
Monitor resolution trends
Address identified gaps promptly
Maintain consistent evaluation criteria

Documentation