Building RAG Systems with Open Models: A Practical Guide

Large language models are incredibly powerful, but they often lack access to your specific, private, or real-time data. Retrieval-Augmented Generation (RAG) solves this problem by connecting open models to your documentation and databases, ensuring accurate and grounded responses. This text-only course guides you from foundational AI concepts to constructing your own fully functional RAG pipeline. You will understand how to process text, generate embeddings, store them in vector databases, and retrieve the most relevant information to guide your language model's outputs. What you'll learn: Understand the core terminology of RAG, vector embeddings, and open-source language models; Chunk and prepare unstructured text data for efficient semantic retrieval; Configure vector databases to store and query high-dimensional data; Apply prompt engineering techniques to guide open models using retrieved context; Implement basic evaluation strategies to measure the accuracy of your RAG system; Explore modern orchestration frameworks to streamline your development workflow. You will begin by exploring the essential concepts of semantic search and language model prompting. Then, you will progress through a step-by-step written workflow, learning how to assemble and refine each component of a modern RAG architecture. This course is designed for software developers, technical builders, and curious learners who are new to AI engineering. No previous experience with machine learning is required, though basic programming familiarity is helpful. Start reading today to unlock the power of open models with your own data.