Conversational ScorerLLM-as-Judge

KnowledgeRetentionScorer

Evaluates how well an AI maintains and utilizes information across conversation turns. Assesses whether the model remembers and correctly applies information shared earlier in the conversation, avoiding contradictions and maintaining consistency.

Overview

Evaluates how well an AI maintains and utilizes information across conversation turns. Assesses whether the model remembers and correctly applies information shared earlier in the conversation, avoiding contradictions and maintaining consistency.

conversationalmemoryllm-judgetrace-evaluationconsistencyquality

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

ParameterTypeRequiredDescription
output_textstrYesThe model's response to evaluate
expected_outputstrNoExpected response for comparison
context.conversationConversationYesConversation object with turns history
context.conversation.turnslist[ConversationTurn]YesList of conversation turns with speaker and message

Output Schema

FieldTypeDescription
scorefloatKnowledge retention score (0-10)
passedboolTrue if retention is adequate
reasoningstrAnalysis of knowledge retention
metadatadictRetention metrics

Score Interpretation

Default threshold: 7/10

9-10ExcellentResponse fully meets all evaluation criteria
7-8GoodResponse meets most criteria with minor issues
5-6FairResponse partially meets criteria, needs improvement
3-4PoorResponse has significant issues
0-2FailingResponse fails to meet basic criteria

Frequently Asked Questions

When should I use this scorer?

Use KnowledgeRetentionScorer when you need to evaluate conversational and memory 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

CategoryConversational
Evaluation TypeLLM-as-Judge
Requires Expected OutputNo
Default Threshold7/10

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