ContextGroundednessScorer
Evaluates how well an answer is grounded in and supported by the provided context. Ensures claims can be traced back to source material. Essential for RAG reliability.
Overview
Evaluates how well an answer is grounded in and supported by the provided context. Ensures claims can be traced back to source material. Essential for RAG reliability.
Use Cases
- RAG-based question answering systems
- Hallucination detection in generated content
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 | Answer to check for groundedness |
| input_text | str | No | Original question |
| context | list[str] | str | Yes | Context to ground against |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Groundedness score (0-10) |
| passed | bool | True if well-grounded |
| reasoning | str | Groundedness analysis |
| metadata | dict | Grounding details |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use ContextGroundednessScorer when you need to evaluate multi-context 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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