Agentic RAG Evaluation: Metrics and Libraries for AI Systems

Building an intelligent AI agent is only the first step; ensuring its responses are accurate, relevant, and safe is where the real challenge begins. This text-based course guides you through the essential methodologies and tools needed to systematically evaluate agentic Retrieval-Augmented Generation (RAG) workflows. You will transition from guessing how well your AI application performs to quantitatively measuring its accuracy, retrieval quality, and generation safety. By studying clear written explanations and practical code snippets, you will learn to implement robust evaluation pipelines that keep your AI agents reliable. What you'll learn: Understand the core concepts of agentic RAG and why traditional evaluation fails; Define key evaluation metrics including faithfulness, answer relevance, and context recall; Apply modern evaluation libraries to automate the testing of your LLM outputs; Implement LLM-as-a-judge patterns to scale your quality assurance workflows; Analyze retrieval performance and optimize vector database queries for better context; Integrate continuous evaluation into your development lifecycle to prevent regressions. The course starts with foundational definitions of agentic workflows and RAG architecture before introducing key metric frameworks. You will then explore step-by-step code implementations of automated evaluation libraries, learning how to run tests and interpret the results. This course is designed for software developers, data analysts, and AI enthusiasts who are new to evaluating language model applications. No advanced background in machine learning is required, though basic familiarity with Python is helpful. Start building more reliable, production-ready AI agents today.