AI Agent Evaluation with RAGAS

RAGAS (RAG ASsessment) is an evaluation framework for RAG pipelines. Here, we see how to use RAGAS for evaluating an AI agent built using LangChain and using Anthropic's Claude 3, Cohere's embedding models, and the Pinecone vector database. 📌 Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/better-rag/03-ragas-evaluation.ipynb 📕 Article: https://www.pinecone.io/learn/series/rag/ragas/ 🌟 Build Better Agents + RAG: https://platform.aurelio.ai (use "JBMARCH2025" coupon code for $20 free credits) 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 00:00 RAG Evaluation 00:39 Overview of LangChain RAG Agent 03:04 RAGAS Code Prerequisites 03:40 Agent Output for RAGAS 05:14 RAGAS Evaluation Format 08:04 RAGAS Metrics 08:56 Understanding RAGAS Metrics 09:16 Retrieval Metrics 11:55 RAGAS Context Recall 14:43 RAGAS Context Precision 15:52 Generation Metrics 16:05 RAGAS Faithfulness 17:16 RAGAS Answer Relevancy 18:40 Metrics Driven Development #ai #artificialintelligence #nlp #chatbot #langchain