GenerationQualityEvaluator
Evaluates generation stage quality with comprehensive metrics including quality, faithfulness, groundedness, factual accuracy, clarity, completeness, consistency, bias, hallucination, alignment, and technical accuracy.
Overview
Evaluates generation stage quality with comprehensive metrics including quality, faithfulness, groundedness, factual accuracy, clarity, completeness, consistency, bias, hallucination, alignment, and technical accuracy.
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 |
|---|---|---|---|
| ground_truth | str | Yes | Original query |
| context.generated_answer | str | Yes | Generated answer to evaluate |
| context.retrieved_contexts | list[str] | Yes | Context used for generation |
| weights | dict | No | Weights for quality dimensions |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Generation quality score (0-10) |
| reasoning | str | Quality analysis |
| details | dict | Detailed quality metrics |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use GenerationQualityEvaluator when you need to evaluate rag and quality 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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