ToneConsistencyScorer
Evaluates tone consistency and appropriateness of responses. Assesses whether tone matches domain, remains consistent throughout, and aligns with conversation history.
Overview
Evaluates tone consistency and appropriateness of responses. Assesses whether tone matches domain, remains consistent throughout, and aligns with conversation history.
Use Cases
- 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 | Response to evaluate for tone |
| input_text | str | Yes | Original prompt |
| context | dict | str | No | May include expected tone/style |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Tone consistency score (0-10) |
| passed | bool | True if tone is appropriate |
| reasoning | str | Tone analysis |
| metadata | dict | Tone characteristics |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use ToneConsistencyScorer when you need to evaluate quality and conversational aspects of your AI outputs. It's particularly useful for conversational ai quality assessment.
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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