RAG with Python: Hybrid Search, Reranking, and Rewriting

Large language models are powerful, but they often lack access to your specific data. Retrieval-Augmented Generation (RAG) bridges this gap, allowing you to connect AI to custom knowledge bases. In this text-based course, you will explore how to build robust RAG systems using Python. Starting with fundamental concepts, you will progress to implementing modern techniques like hybrid search, result reranking, and query rewriting to drastically improve the accuracy of AI responses. What you will learn: Understand the core architecture and terminology of Retrieval-Augmented Generation; Build foundational RAG pipelines using Python and LangChain; Implement vector databases to store and retrieve document embeddings; Apply hybrid search techniques combining keyword and semantic retrieval; Improve AI accuracy using result reranking and query rewriting patterns; Explore modern Agentic AI concepts for autonomous data retrieval. The course begins with clear definitions of AI terminology before moving into practical Python implementations. You will read through detailed explanations, examine code snippets, and practice building text-based AI retrieval systems step by step. This course is designed for beginners and aspiring developers; no prior experience with advanced machine learning is required. Start reading today to unlock the power of modern RAG and build smarter data-driven applications.