Measuring RAG Faithfulness and Answer Relevance with Ragas

Integrate continuous evaluation metrics into your CI/CD pipeline to catch hallucination regressions before shipping.

Prerequisites

Python
Docker

Step 1: Step 1: The Four Pillars of RAG Triad Evaluation

Deconstruct quality into: Context Precision, Context Recall, Faithfulness (groundedness in context), and Answer Relevance.

Step 2: Step 2: Automated GitHub Action Pull Request Gate

Block PR merges if test dataset faithfulness drops below 0.90 on new model or chunking configurations.