RAGAS
A framework for evaluating RAG systems with metrics like context relevance, faithfulness, and answer relevance.
Think of it like
Like a quality checklist for a restaurant: checks for cleanliness, food freshness, and service.
Example
RAGAS generates a score from 0–1 that aggregates retrieval quality, model faithfulness, and answer relevance.
How it actually works
RAGAS is designed for RAG systems where retrieval + generation matter. It combines multiple metrics (context relevance, faithfulness, answer relevance, semantic similarity). It's automated (no human raters needed) but relies on LLM judges, so it's not perfect. Useful for rapid iteration and regression testing.
For product teams
RAGAS is a quick way to check RAG quality end-to-end.
For engineers
Use RAGAS for continuous monitoring, but validate against human eval on your domain.
Related
- Context Relevance — What it measures.
- Faithfulness — What it measures.
- Answer Relevance — What it measures.
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