RAG and Agentic AI Project Guide: Build a Complete AI System

As organizations rush to adopt artificial intelligence, the ability to build reliable, context-aware AI systems is one of the most sought-after skills in technology today. This text-based course guides you through the process of designing a robust Retrieval-Augmented Generation (RAG) and Agentic AI system from the ground up. By working through this comprehensive guide, you will transition from understanding basic generative AI to structuring complex workflows that connect vector databases, embeddings, and autonomous decision-making agents. You will gain the practical knowledge needed to plan, build, and evaluate production-style AI applications. What you'll learn: - Understand the fundamental terminology of RAG, vector embeddings, and autonomous agent architectures. - Create and manage structured datasets using JSON to feed your retrieval pipelines. - Generate text and image embeddings to support multimodal search capabilities. - Configure retrieval logic and connect your system to modern vector databases. - Design agentic workflows where LLMs make decisions and call tools dynamically. - Evaluate your system's performance using modern RAG assessment methodologies. The course starts with essential definitions and foundational AI concepts before guiding you step-by-step through dataset preparation, embedding generation, and agent integration. You will learn through detailed written explanations, conceptual breakdowns, and clear code snippets. This course is designed for aspiring AI developers, software engineers, and data enthusiasts who want a structured, beginner-friendly introduction to building advanced AI systems without complex prerequisites. Start reading today and learn how to construct intelligent, agentic systems step by step.