Advanced RAG Pipelines: Retrieval and Evaluation Strategies

Standard retrieval-augmented generation (RAG) often falls short when handling complex, real-world data queries. To build production-ready AI applications, you need sophisticated retrieval strategies and robust evaluation frameworks that ensure accuracy and minimize hallucinations. This course guides you from the foundational mechanics of semantic search to advanced, high-performance RAG architectures. You will learn how to implement superior retrieval techniques and objectively measure your system's performance. What you'll learn: - Understand the core limitations of naive RAG and the mathematical foundations of vector embeddings - Implement advanced retrieval strategies including sentence-window retrieval and auto-merging context techniques - Configure chunking strategies and metadata routing to improve retrieval precision - Evaluate RAG performance systematically using key metrics like faithfulness, answer relevance, and context recall - Apply modern debugging workflows to isolate and resolve retrieval failures and generation errors Starting with essential terminology and vector database fundamentals, you will progress through step-by-step written explanations of advanced retrieval algorithms and structured evaluation methodologies. This text-based course is designed for software engineers, data analysts, and AI enthusiasts who have a basic familiarity with Python and want to build highly reliable LLM applications. No prior experience with advanced search architectures is required. Start reading today to elevate your AI retrieval systems to production standards.