ContextualRecallScorer
Measures recall of retrieved context by estimating what fraction of relevant information was successfully retrieved. Helps identify gaps in retrieval coverage.
Overview
Measures recall of retrieved context by estimating what fraction of relevant information was successfully retrieved. Helps identify gaps in retrieval coverage.
Use Cases
- RAG-based question answering systems
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 generated answer |
| input_text | str | Yes | The original query |
| context | dict | list[str] | Yes | Retrieved chunks |
| expected_output | str | No | Ground truth (helps estimate coverage) |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Recall score (0-10) |
| passed | bool | True if recall acceptable |
| reasoning | str | Coverage analysis |
| metadata.estimated_total_relevant | int | Estimated relevant info |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use ContextualRecallScorer when you need to evaluate rag and llm-judge 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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