Foundations of RAG Pipelines and LLMOps

As AI applications evolve, simply prompting a language model is no longer enough. To build reliable, context-aware AI tools, you need Retrieval-Augmented Generation (RAG) and robust operational practices (LLMOps). This course breaks down the complex world of modern AI engineering into manageable, text-based lessons. You will start with foundational terminology and progress to understanding how to design, deploy, and monitor an end-to-end RAG system using current industry standards. What you'll learn: • Understand the fundamental architecture of Retrieval-Augmented Generation (RAG). • Explore modern vector databases and how they store and retrieve semantic data. • Apply basic prompt engineering techniques to improve model accuracy and reduce hallucinations. • Design a basic text pipeline for ingesting, chunking, and processing documents. • Learn essential LLMOps concepts, including deployment strategies and performance monitoring. The course begins with core definitions and basic AI concepts before moving into practical architecture design and deployment strategies. You will read through clear explanations and analyze written code snippets that demonstrate how these systems are built in the real world. This foundational course is designed entirely for beginners—no prior machine learning experience is required, just a basic understanding of software concepts. Start reading today and take your first step into the world of production AI engineering.