QueryProcessingEvaluator
Evaluates query understanding and processing quality. Assesses clarity, intent detection, preprocessing effectiveness, specificity, complexity, and ambiguity of input queries with configurable weights.
Overview
Evaluates query understanding and processing quality. Assesses clarity, intent detection, preprocessing effectiveness, specificity, complexity, and ambiguity of input queries with configurable weights.
Use Cases
- RAG-based question answering systems
How It Works
This scorer uses LLM-as-Judge technology to evaluate responses. It prompts a large language model with specific evaluation criteria and the content to assess, then analyzes the LLM's judgment to produce a score and detailed reasoning.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| prediction | str | Yes | Query to evaluate |
| weights | dict | No | Weights for different aspects (clarity, specificity, etc.) |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Query quality score (0-10) |
| reasoning | str | Query analysis |
| details | dict | Per-aspect scores |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use QueryProcessingEvaluator when you need to evaluate rag and rule-based aspects of your AI outputs. It's particularly useful for rag-based question answering systems.
Why doesn't this scorer need expected output?
This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.
Can I customize the threshold?
Yes, the default threshold of 7 can be customized when configuring the scorer.
Quick Info
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