Ragas on AI Knowledge Base

📚 Core Concepts

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🚀 Get Started

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Adapting Metrics to Target Language

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Adding to your CI pipeline with Pytest

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AG-UI

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AG-UI Integration

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Agent Evaluation Quickstart

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Agentic or Tool use

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Align an LLM as a Judge

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Aligning LLM Evaluators with Human Judgment

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Amazon Bedrock

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Answer correctness

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Applications

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Arize

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Aspect Critique

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Athina AI

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Caching

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Cancelling Tasks

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Comet Opik

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Compare Embeddings for retriever

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Compare LLMs using Ragas Evaluations

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Compare models provided by VertexAI on RAG-based Q&A task using Ragas metrics

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Components Guide

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Context Entities Recall

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Context Precision

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Context Recall

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Cost Analysis

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Custom Multi-hop Query

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Custom Single-hop Query

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Customise models

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Customizations

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Customizing Test Data Generation

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Datasets

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DSPy Optimizer

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Evaluate a New LLM

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Evaluate a prompt

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Evaluate a simple LLM application

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This is the canonical first hands-on with RAGAS, and it matters because it shows the metric-driven evaluation loop on a simple LLM app before you add retrieval complexity. Focus on how RAGAS frames a sample, a metric, and a score — the same abstractions scale up to full RAG evaluation. A subtle gotcha is that many RAGAS metrics call an LLM under the hood, so scores carry cost and run-to-run variance you must account for. Read this before the RAG tutorial, which layers retrieval metrics on top.

Evaluate a simple RAG system

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Evaluate a simple RAG system

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This is the practical RAG evaluation walkthrough and the page most teams should run first when they need to measure a retrieval pipeline rather than guess at it. Pay attention to the distinction between retrieval metrics like context precision and recall and generation metrics like faithfulness and answer relevancy, because a RAG system can fail at either stage and the fix differs entirely. A common mistake is optimizing answer quality while ignoring context recall, leaving the model fluent but ungrounded. Start with the simple-evals page first if you are new to RAGAS.

Evaluate a Text-to-SQL Agent

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Evaluate an AI Agent

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Evaluate an AI Workflow

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Evaluate and Improve a RAG App

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Evaluating Multi-turn Conversations

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Evaluation Dataset

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Evaluation Sample

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Evaluations with Vertex AI models

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Experimentation

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Factual Correctness

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Faithfulness

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General Purpose Metrics

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Google Gemini

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Griptape

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Haystack

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Haystack Integration

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Helicone

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Improve RAG

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Installation

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Integrations

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Iterate and Improve Prompts

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Judge Alignment Quickstart

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Langchain

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LangChain

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Langfuse

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LangGraph

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Langsmith

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LangSmith

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List of available metrics

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LlamaIndex

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LlamaIndex Agent Evaluation Quickstart

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LlamaIndex Agents

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LlamaStack

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LLM Benchmarking Quickstart

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Metrics

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Modify Prompts

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Multi modal faithfulness

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Multi modal relevance

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Noise Sensitivity

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Non-English Testset Generation

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Nvidia Metrics

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OCI Gen AI

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Openlayer

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Overview

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Persona Generation

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Prompt

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Prompt Evaluation Quickstart

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Quick Start

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R2R

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RAG Evaluation

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Ragas CLI

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Response Relevancy

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Rubric-Based Evaluation

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Run Config

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Run your first experiment

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Semantic Similarity

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Single-hop Query Testset

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SQL

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Summarization

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Swarm

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Systematic Prompt Optimization

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Testset Generation

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Testset Generation for Agents or Tool use cases

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Testset Generation for RAG

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Testset Generation for RAG

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Text-to-SQL Evaluation Quickstart

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Tonic Validate

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Tracing and logging evaluations with Observability tools

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Traditional NLP Metrics

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Tutorials

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Understand Cost and Usage of Operations

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Using Pre-chunked Data

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Utilizing User Feedback

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Workflow Evaluation Quickstart

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Zeno

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