Building RAG Systems: Search, Reranking, and Evaluation

As Large Language Models (LLMs) transform software development, the ability to connect them to custom data is becoming a critical skill. Retrieval-Augmented Generation (RAG) is the industry standard for making AI responses accurate and context-aware. This text-based course guides you through building a RAG system from the ground up. You will start with the core terminology of AI and vector search, then progress to implementing hybrid search, applying reranking techniques, and evaluating the quality of your system's answers using modern Python patterns. What you'll learn: - Understand the foundational concepts of Retrieval-Augmented Generation and embedding models. - Build custom data pipelines to prepare and ingest text into modern vector databases. - Implement hybrid search strategies combining keyword and semantic retrieval. - Apply reranking algorithms to improve the relevance of retrieved context. - Evaluate the accuracy and quality of LLM-generated responses using current industry metrics. - Practice integrating prompt engineering basics to optimize AI outputs. The curriculum flows logically from basic definitions and core AI concepts to practical implementation steps. You will read clear explanations and study well-structured code snippets to build your understanding of modern AI backend integration. Designed for beginners and developers with basic programming knowledge, this course requires no prior machine learning experience. Start reading today to build your foundational skills in Retrieval-Augmented Generation.