QuestionAnswerAlignmentScorer
Evaluates alignment between questions and answers. Assesses how well the answer directly addresses the question, stays relevant, and provides appropriate detail level.
Overview
Evaluates alignment between questions and answers. Assesses how well the answer directly addresses the question, stays relevant, and provides appropriate detail level.
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 |
|---|---|---|---|
| output_text | str | Yes | Answer to evaluate |
| input_text | str | Yes | Original question |
| context | dict | str | No | Additional context |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Alignment score (0-10) |
| passed | bool | True if well-aligned |
| reasoning | str | Alignment analysis |
| metadata.raw_score | float | Raw alignment value |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use QuestionAnswerAlignmentScorer when you need to evaluate quality and rag 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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