Foundations of Local RAG: Chat with Your Own Documents

Are you looking for a way to use large language models with your own private data without sending sensitive information to external servers? Building a local Retrieval-Augmented Generation (RAG) system is the perfect solution for turning your documents into a secure, searchable knowledge base. This course guides you through the process of connecting your personal or business text files to an AI model. You will understand how to process your data, store it efficiently, and retrieve the right context so your AI can provide accurate, document-backed answers. What you'll learn: - Understand the core concepts of Retrieval-Augmented Generation and why it reduces AI hallucinations. - Process and chunk your text documents to prepare them for modern embedding models. - Build a foundational vector database to store and efficiently search your document embeddings. - Create retrieval pipelines that fetch the most relevant information for user queries. - Apply basic prompt engineering techniques to format retrieved data for the language model. - Design a secure, local workflow that keeps your private data safe from external APIs. The curriculum begins with essential AI terminology and foundational RAG concepts before moving into practical, written exercises. You will read through step-by-step code snippets and architectural patterns that show you exactly how data flows from a raw document to a final AI-generated answer. This course is designed for beginners, IT professionals, and researchers who want to explore local AI deployment—no prior machine learning experience is required. Start building your own private document assistant today.