ConversationRelevancyScorer
Assesses how relevant each response is to the current conversation context and user query. Evaluates whether responses stay on topic, address the user's actual question, and maintain thematic coherence with the ongoing dialogue.
Overview
Assesses how relevant each response is to the current conversation context and user query. Evaluates whether responses stay on topic, address the user's actual question, and maintain thematic coherence with the ongoing dialogue.
Use Cases
- RAG-based question answering systems
- Conversational AI quality assessment
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 model's response to evaluate |
| expected_output | str | No | Expected response for comparison |
| context.conversation | Conversation | Yes | Conversation object with turns history |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Relevancy score (0-10) |
| passed | bool | True if sufficiently relevant |
| reasoning | str | Relevancy analysis |
| metadata | dict | Relevancy details |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use ConversationRelevancyScorer when you need to evaluate conversational 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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