Connect AI to Custom Data with RAG

Are you looking to build AI applications that can reason about your specific documents or internal knowledge? Large language models are powerful, but they lack access to your private data. This course provides a practical foundation in Retrieval-Augmented Generation (RAG), the key technique for connecting LLMs to custom data sources. You will learn how to build a complete RAG pipeline, enabling you to create chatbots and question-answering systems that provide accurate, context-aware responses based on your own information, effectively reducing inaccuracies and knowledge gaps. What you'll learn: - Understand the core concepts of RAG, including embeddings, vector stores, and the retrieval-generation workflow. - Learn to process and prepare your own documents for ingestion into a RAG system. - Build a complete question-answering pipeline using popular Python frameworks and tools. - Practice creating vector indexes from your text data for efficient semantic search. - Apply basic prompt engineering techniques to guide the LLM in generating faithful, context-based answers. - Explore simple methods for evaluating the performance and accuracy of your RAG system. The course starts with the fundamental theory behind RAG before guiding you through the practical steps of building, querying, and evaluating a system with hands-on exercises. This course is designed for developers and tech enthusiasts new to building with LLMs. No prior experience in AI or machine learning is required, though basic Python programming knowledge is recommended. Start learning today and unlock the power of your own data with AI.