RAGAS

Reference-free evaluation framework specifically for RAG pipelines

Data & AnalyticsFree

RAGAS (Retrieval Augmented Generation Assessment) is an open-source framework for evaluating the performance of RAG (retrieval-augmented generation) pipelines without requiring human-annotated ground truth labels. It measures faithfulness, answer relevancy, context precision, context recall, and context entity recall using LLM-as-a-judge approaches. RAGAS is widely used by teams building document Q&A systems, enterprise chatbots, and AI search products to benchmark and optimize retrieval and generation quality.

Key Features

• ✓Reference-free evaluation
• ✓RAG-specific metrics
• ✓LLM-as-judge
• ✓Faithfulness scoring
• ✓Context evaluation
• ✓Open source