FaithfulnessScorer
Measures how faithful the generated answer is to the provided context. Critical RAG metric that evaluates whether the answer is grounded in and supported by retrieved documents, detecting hallucinations and unsupported claims.
Overview
Measures how faithful the generated answer is to the provided context. Critical RAG metric that evaluates whether the answer is grounded in and supported by retrieved documents, detecting hallucinations and unsupported claims.
Use Cases
- RAG-based question answering systems
- Safety and compliance monitoring
- 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 | The generated answer to verify |
| input_text | str | No | The original question |
| context | dict | str | list | Yes | Retrieved context/documents |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Faithfulness score (0-10) |
| passed | bool | True if faithful to context |
| reasoning | str | Claim verification details |
| metadata.claims | list | Extracted claims and verification status |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use FaithfulnessScorer when you need to evaluate rag and hallucination 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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