RAGAS: Open Source LLM Evaluation Framework Tutorial for 2026

RAGAS is the leading open-source framework for evaluating RAG pipelines with 14,855 GitHub stars. This tutorial covers both metric families - retrieval (context precision/recall) and generation (faithfulness/answer relevancy/correctness) - plus installation, dataset creation, and judge-LLM (GPT-4 or Claude) setup.

💡 What You Will Learn

RAGAS is the leading open-source framework for evaluating RAG pipelines with 14,855 GitHub stars. This tutorial covers both metric families - retrieval (context precision/recall) and generation (faith

📜 Table of Contents

• What RAGAS Measures
• Retrieval Metrics
• Generation Metrics
• Installation
• Creating a Dataset
• Running Evaluation
• Interpreting Scores

RAGAS (RAG Assessment) is the leading open source framework for evaluating RAG pipelines. With 14,855 stars on GitHub, it is the tool most AI engineers reach for.

What RAGAS Measures

Retrieval Metrics

• Context Precision: Are top-ranked docs the most relevant?
• Context Recall: Did we retrieve all relevant docs?

Generation Metrics

• Faithfulness: Does answer stay within context?
• Answer Relevancy: Does answer address the question?
• Answer Correctness: Is answer factually correct?

Installation

pip install ragas

You need a judge LLM (GPT-4 or Claude) to score answers.

import os
os.environ["OPENAI_API_KEY"] = "your-key-here"

Creating a Dataset

from datasets import Dataset

data = {
    "question": ["What is RAG?"],
    "answer": ["RAG stands for Retrieval-Augmented Generation..."],
    "contexts": [["RAG is a technique that..."]],
    "ground_truth": ["RAG is..."]
}
dataset = Dataset.from_dict(data)

Running Evaluation

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

result = evaluate(dataset=dataset, metrics=[faithfulness, answer_relevancy])
print(result)

Interpreting Scores