AnswerRelevancyScorer
Evaluates how relevant the generated answer is to the input question. Assesses whether the response directly addresses what was asked, stays on topic, and provides useful information. Fundamental RAG metric.
Overview
Evaluates how relevant the generated answer is to the input question. Assesses whether the response directly addresses what was asked, stays on topic, and provides useful information. Fundamental RAG metric.
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 | The generated answer |
| input_text | str | Yes | The original question/query |
| context | dict | str | list | No | Retrieved context (optional for relevancy) |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Answer relevancy score (0-10) |
| passed | bool | True if relevant |
| reasoning | str | Relevancy analysis |
| metadata | dict | Similarity metrics |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use AnswerRelevancyScorer 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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