---
title: "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."
verification: "listed"
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

Choose whichever fits your setup:

1. Copy this skill folder into your local skills directory.
2. Clone the repo and symlink or copy the skill into your agent workspace.
3. Add the repo as a git submodule if you manage shared skills centrally.
4. Install it through your internal provisioning or packaging workflow.
5. Download the folder directly from GitHub and place it in your skills collection.

Install command or upstream instructions:

```
Install Ragas in a Python environment, configure a supported model provider, prepare evaluation samples or generate a testset, then run the documented evaluation flow against the target RAG pipeline.
```

## 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/)