RAGAS Framework
RAGAS evaluation framework for RAG: faithfulness, answer relevancy, context precision, and automated quality scoring.
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RAGAS Framework — Complete Guide
RAGAS Framework — Complete Guide
1. Introduction to RAGAS
RAGAS (Retrieval Augmented Generation Assessment) is a framework specifically designed to evaluate RAG pipelines without requiring human-annotated ground truth labels. Created by Exploding Gradients in 2023, RAGAS addresses a critical gap: how do you evaluate a RAG system when you don't have "correct answers" to compare against?
Key Insight: Traditional NLP metrics (BLEU, ROUGE) require reference answers. RAGAS uses LLM-as-judge to evaluate quality aspects (faithfulness, relevancy) that would otherwise need expensive human annotation.
Why RAGAS Was Created
- No Ground Truth Problem: In production RAG, you often don't have "correct" answers for every query
- Multi-Component Evaluation: RAG has 2 stages (retrieval + generation) that both need evaluation
- LLM-Native Metrics: Traditional metrics like BLEU don't capture semantic similarity or factual correctness
- Automation Need: Manual evaluation doesn't scale to thousands of queries