Evaluating Enterprise RAG Systems for Reliable AI Outputs

Building a Retrieval-Augmented Generation (RAG) system is only the first step; ensuring its outputs are accurate, safe, and contextually grounded is where true enterprise readiness begins. This text-based course guides you through the essential methodologies to measure, test, and improve your RAG pipeline's performance. You will transition from guessing if your AI is performing well to systematically measuring its success, learning how to isolate retrieval failures from generation hallucinations to ensure your enterprise AI remains trustworthy. What you'll learn: Understand the core metrics of RAG evaluation, including faithfulness, answer relevance, and context recall; Implement automated evaluation strategies using modern LLM-as-a-judge patterns; Differentiate between retrieval errors and generation hallucinations to target pipeline improvements; Apply systematic testing to vector database retrieval and prompt templates; Establish baseline datasets and ground-truth benchmarks for continuous monitoring. The course begins with foundational RAG concepts and evaluation terminology before moving into practical, step-by-step guidance on setting up automated evaluation workflows. You will read through clear explanations and code-based examples designed to be applied immediately to your projects. This course is designed for software developers, product managers, and AI enthusiasts who want to move past basic prototyping. No advanced machine learning background is required. Start building reliable, production-ready AI systems by mastering the art of RAG evaluation today.