RAGAS
FreeReference-free evaluation framework specifically for RAG pipelines.
Tool Info
Overview
RAGAS provides metrics designed specifically for retrieval-augmented generation systems.
It measures faithfulness, answer relevance, and context quality without human labels.
The de facto standard for RAG evaluation in research and production.
Features
- Faithfulness and relevance metrics
- Test set generation
- LangChain integration
- Async evaluation
Pricing
Pros
- RAG-specific gold standard
- No labeled data required
- Active research community
Best For
When NOT to Use
- LLM-judge costs
- Less general than DeepEval
Typical Users
Community Insights
Real implementation experiences shared by AI practitioners.
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Alternatives
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Tags
Related Guides
- RAG Evaluation
Evaluating RAG systems — faithfulness, answer relevance, context precision/recall, and attributing failures to retrieval vs generation.
- Retrieval Evaluation
Measuring retrieval quality - recall@k, MRR, nDCG, and building golden test sets for RAG pipelines.
- RAG
A comprehensive guide to RAG - the dominant pattern for building AI applications that answer questions using your own data.
- LLM Evaluation
Measuring LLM output quality — automated checks, rubrics, LLM-as-judge, human calibration, and CI eval pipelines for generation.
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