ConversationCompletenessScorer
Evaluates whether responses fully address all aspects of the user's query within the conversation context. Checks for comprehensive coverage of multi-part questions, proper handling of implicit requirements, and appropriate depth of response.
Overview
Evaluates whether responses fully address all aspects of the user's query within the conversation context. Checks for comprehensive coverage of multi-part questions, proper handling of implicit requirements, and appropriate depth of response.
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 | The model's response to evaluate |
| expected_output | str | No | Expected complete response |
| context.conversation | Conversation | Yes | Conversation object with turns history |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Completeness score (0-10) |
| passed | bool | True if response is complete |
| reasoning | str | Coverage analysis |
| metadata | dict | Missing aspects if any |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use ConversationCompletenessScorer when you need to evaluate conversational and quality 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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