AI Engineer Foundations: Retrieval-Augmented Generation

Standard large language models are limited by their training data, but Retrieval-Augmented Generation (RAG) allows them to access up-to-date, proprietary information. This text-based course teaches you how to bridge the gap between static models and dynamic data sources to build intelligent, context-aware AI applications. You will transition from understanding basic generative AI constraints to deploying robust, data-connected systems. What you'll learn: - Understand the core architecture of RAG systems and how they enhance standard language models - Process, chunk, and embed unstructured text data into semantic vector spaces - Query vector databases to retrieve relevant context for user prompts - Implement advanced retrieval strategies, including reranking and metadata filtering - Explore modern multimodal RAG concepts to handle both text and image data - Evaluate and test the accuracy and safety of your retrieval-augmented outputs You will begin by mastering essential AI engineering terminology and foundational data concepts before moving into step-by-step implementation workflows. This course is designed for software developers and tech-savvy beginners who want to break into the AI space; no prior machine learning experience is required. Start reading today to build smarter, context-rich AI applications.