Score Rag Answer Quality And Retrieval Quality Before Rollout With Ragas

---
name: "Score RAG answer quality and retrieval quality before rollout with Ragas"
slug: "score-rag-answer-quality-and-retrieval-quality-before-rollout-with-ragas"
description: "Measure whether a RAG change actually improved answers and retrieval, instead of guessing from a few spot checks."
github_stars: 13412
verification: "security_reviewed"
source: "https://github.com/vibrantlabsai/ragas"
author: "Vibrant Labs AI"
publisher_type: "organization"
category: "Security & Verification"
framework: "Multi-Framework"
tool_ecosystem:
  github_repo: "vibrantlabsai/ragas"
  github_stars: 13412
---

# Score RAG answer quality and retrieval quality before rollout with Ragas

Measure whether a RAG change actually improved answers and retrieval, instead of guessing from a few spot checks.

## Prerequisites

Python environment, Ragas package, model provider credentials, evaluation dataset or testset generation inputs, access to the target RAG workflow

## Installation

Use the upstream install or setup path that matches your environment:
- pip install ragas
- pip install git+https://github.com/vibrantlabsai/ragas

Requirements and caveats from upstream:
- <a href="https://www.python.org/">
- <img alt="Made with Python" src="https://img.shields.io/badge/Made%20with-Python-1f425f.svg?color=purple">
- python

Basic usage or getting-started notes:
- <a href="#fire-quickstart">Quick start</a> |
- ### Clone a Complete Example Project
- ragas comes with pre-built metrics for common evaluation tasks. For example, Aspect Critique evaluates any aspect of your output using DiscreteMetric:

- Source: https://github.com/vibrantlabsai/ragas
- Extracted from upstream docs: https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/README.md

## Documentation

- https://docs.ragas.io/en/stable/

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/score-rag-answer-quality-and-retrieval-quality-before-rollout-with-ragas/)