---
title: Evaluation Frameworks for RAG Systems Introduced
url: https://www.dataloco.com/en/evaluation-frameworks-for-rag-systems-introduced
published: 2026-09-12T21:10:18+00:00
language: en
section: Developers
source: https://machinelearningmastery.com/understanding-rag-part-iv-ragas-evaluation-framework/
publisher: Dataloco
---

# Evaluation Frameworks for RAG Systems Introduced

Recently, insights into evaluation frameworks for retrieval augmented generation systems have been shared. These frameworks aim to enhance the assessment of performance for large language model applications. The frameworks include DeepEval, which provides over 14 metrics focusing on aspects like hallucination and faithfulness, and MLflow LLM Evaluate, which is celebrated for its modularity in custom evaluations. Another significant framework is RAGAs, or retrieval augmented generation assessment, noted for its comprehensive approach to evaluating RAG systems.

RAGAs stands out due to its ability to assess both the retriever and generator components of a RAG system. This evaluation can occur in isolation or as part of a unified pipeline. A key feature of RAGAs is its metric-driven development approach, which emphasizes data-driven decision-making and continuous monitoring of critical performance metrics over time.

The framework allows for the configuration of specific metrics for both the retriever and the generator, ultimately blending them into an overall RAGAs score. This scoring process involves selecting relevant metrics, normalizing them, and computing a weighted average based on the priorities of each use case. This facilitates a nuanced understanding of system performance, especially crucial for applications requiring high factual accuracy.