ContextRelevancyScorer
Evaluates how well the agent maintains and utilizes relevant context throughout the interaction. Assesses whether responses are appropriate given the agent's task, role, and available information. Also useful for RAG context evaluation.
Overview
Evaluates how well the agent maintains and utilizes relevant context throughout the interaction. Assesses whether responses are appropriate given the agent's task, role, and available information. Also useful for RAG context evaluation.
Use Cases
- RAG-based question answering systems
- Autonomous AI agent evaluation
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 |
|---|---|---|---|
| agent_data.agent_task | str | Yes | The task assigned to the agent |
| agent_data.agent_role | str | Yes | The role of the agent |
| agent_data.agent_response | str | Yes | The response from the agent |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Score (0-10 scale) |
| passed | bool | True if score meets threshold |
| reasoning | str | Detailed evaluation explanation |
| metadata | dict | Scorer-specific details |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use ContextRelevancyScorer when you need to evaluate agent 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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