Explore the RAGAS (RAG ASsessment) evaluation framework for RAG pipelines in this 20-minute video tutorial. Learn how to assess an AI agent built with LangChain, utilizing Anthropic's Claude 3, Cohere's embedding models, and the Pinecone vector database. Dive into the process of evaluating RAG systems, understanding RAGAS metrics, and implementing metrics-driven development. Gain insights into retrieval metrics like context recall and precision, as well as generation metrics such as faithfulness and answer relevancy. Access the accompanying code, article, and additional resources to enhance your understanding of RAG evaluation techniques.
Overview
Syllabus
RAG Evaluation
Overview of LangChain RAG Agent
RAGAS Code Prerequisites
Agent Output for RAGAS
RAGAS Evaluation Format
RAGAS Metrics
Understanding RAGAS Metrics
Retrieval Metrics
RAGAS Context Recall
RAGAS Context Precision
Generation Metrics
RAGAS Faithfulness
RAGAS Answer Relevancy
Metrics Driven Development
Taught by
James Briggs
Related Courses
-
Fundamentals of AI Agents Using RAG and LangChain
-
Hallucination Detection and Evaluation in RAG Systems - Using RAGAS and Lynx
-
LangChain v1 XML Agents - Building Conversational AI with Claude 2.1, Cohere, and Pinecone
-
LangChain Multi-Query Retriever for RAG - Advanced Technique for Broader Vector Space Search
-
Retrieval Augmented Generation with LangChain: ChatGPT for Your Data - Part 1
