RAG Evaluation Basics: Measure Retrieval Quality with Ragas

Building a Retrieval-Augmented Generation (RAG) application is only the first step; ensuring it consistently returns accurate and relevant answers is the real challenge. Without proper evaluation, AI systems can easily hallucinate or retrieve irrelevant context, leading to poor user experiences. This text-based course guides you through the essential concepts of RAG evaluation and modern observability. You will learn how to systematically measure the performance of your retrieval pipelines, identify failure points, and apply targeted fixes to improve overall response quality. What you'll learn: - Understand foundational RAG concepts, including vector databases and modern retrieval patterns. - Apply the Ragas framework to measure key metrics like context precision, recall, and answer relevancy. - Integrate Langfuse to trace LLM executions and monitor pipeline performance effectively. - Identify common retrieval failures and practice strategies to mitigate AI hallucinations. - Implement prompt engineering basics to refine generation quality and control outputs. - Establish foundational MLOps practices for continuous evaluation of your AI models. The material begins with core terminology and foundational definitions before progressing into practical, written exercises. You will read through step-by-step code snippets and realistic scenarios that demonstrate how to set up robust evaluation workflows from scratch. Designed for beginners and aspiring ML engineers, this course requires no prior experience with evaluation frameworks, making it accessible to anyone familiar with basic programming concepts. Start reading today to ensure your AI applications deliver reliable, high-quality results.