Retrieval-Augmented Generation (RAG) Fundamentals and Practical Applications

Large Language Models often lack specific, timely, or proprietary knowledge. This course teaches you how to bridge that gap using Retrieval-Augmented Generation (RAG) techniques. By the end of this course, you will understand the architecture of modern RAG systems and possess the practical skills to build, test, and deploy your own data-driven AI applications using industry-standard tools like LangChain and Streamlit. What you'll learn: * Understand the core components of RAG, including indexing, document retrieval, and response generation. * Practice effective data chunking strategies and embedding techniques for specialized knowledge bases. * Implement RAG workflows using the LangChain orchestration framework to connect components seamlessly. * Configure prompt templates to ensure context relevance and instruction following for accurate outputs. * Build user interfaces for your RAG applications using the Streamlit framework. * Apply basic evaluation methods to assess the relevance and faithfulness of generated responses. The material begins by defining RAG terminology and foundational concepts, then transitions into hands-on exercises focused on setting up data pipelines, integrating Large Language Models, and developing interactive front-ends. This course is designed for beginners who have basic Python knowledge and want to enter the field of applied AI. No prior experience with LangChain, Streamlit, or LLM deployment is required. Start mastering the practical skills needed to build the next generation of knowledge-aware AI tools.