Ragas is a library that helps you move from "vibe checks" to systematic evaluation loops for your AI applications. It provides tools to supercharge the evaluation of Large Language Model …

Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the …

Dec 4, 2025 · Ragas Office Hours - If you need help setting up Evals for your AI application, sign up for our Office Hours here.

For an in-depth explanation of the core concepts behind Ragas, check out the Core Concepts page. You can also explore the How-to Guides for specific applications of Ragas.

: Ragas Metrics Use our library of available metrics or create custom metrics tailored to your use case. Metrics for evaluating RAG, Agentic workflows and more... Test Data Generation …

The how-to guides offer a more comprehensive overview of all the tools Ragas offers and how to use them. This will help you tackle messier real-world usecases when you’re using the …

The purpose of this guide is to illustrate a simple workflow for testing and evaluating an LLM application with ragas. It assumes minimum knowledge in AI application building and evaluation.

In Ragas, we categorize metrics based on the type of output they produce. This classification helps clarify how each metric behaves and how its results can be interpreted or aggregated.

Each guide in this section provides a focused solution to real-world problems that you, as an experienced user, may encounter while using Ragas. These guides are designed to be …

from ragas.dataset_schema import SingleTurnSample from ragas.metrics import Faithfulness sample = SingleTurnSample( user_input="When was the first super bowl?", response="The …