Getting Started with RAG: Connect AI to Your Private Data

Off-the-shelf AI models are powerful, but they lack access to your private files, internal wikis, and custom databases. Implementing Retrieval-Augmented Generation (RAG) is the key to bridging this gap and making AI work with your specific data. This text-based course guides you through the process of designing and building RAG systems. You will transition from understanding basic generative AI limitations to constructing a reliable pipeline that retrieves relevant document text and uses it to generate accurate, context-aware answers. What you'll learn: - Understand the core architecture of Retrieval-Augmented Generation and how it improves model accuracy. - Prepare and clean document data using modern text chunking and splitting strategies. - Generate vector embeddings to represent semantic meaning within your documents. - Configure a vector database to store, index, and query your embedded document segments. - Apply prompt engineering techniques to ground the AI's responses strictly in your retrieved source materials. - Evaluate the retrieval quality and generation accuracy to prevent hallucinations. You will begin by learning the essential terminology of vector spaces, embeddings, and semantic search before moving on to step-by-step written guides for building a functional RAG pipeline. This course is designed for developers, data professionals, and tech enthusiasts who want to build custom AI applications. No prior experience with vector databases or natural language processing is required. Start reading today to unlock the full potential of AI on your own document library.