Generative AI Engineering: Building LLM, RAG, and Agentic Systems

Generative AI is shifting from simple prompts to sophisticated engineering pipelines that power autonomous applications. Understanding how to connect large language models to real-world data and external tools is now an essential skill for modern developers. In this comprehensive written guide, you will transition from a beginner to a capable AI practitioner who understands how to design, build, and orchestrate complex AI workflows. You will learn the mechanics behind modern retrieval systems and multi-agent setups, enabling you to build applications that can reason, plan, and execute tasks. What you'll learn: - Understand foundational LLM concepts, prompt engineering baselines, and core terminology. - Design Retrieval-Augmented Generation (RAG) systems to connect models with private vector databases. - Build orchestrations and structured workflows using LangChain and LangGraph. - Configure multi-agent systems that collaborate to solve complex, multi-step problems. - Apply Model Context Protocol (MCP) to seamlessly integrate AI models with external tools and data sources. - Explore the fundamentals of model fine-tuning to adapt models for specific domain tasks. The course begins with foundational concepts and terminology before guiding you step-by-step through vector databases, agentic frameworks, and advanced tool integrations. You will read clear explanations and analyze code snippets designed to build your practical understanding of AI architecture. This course is designed for software developers, technical product managers, and technology enthusiasts who are new to AI engineering and want a solid, conceptual, and practical foundation without requiring prior machine learning experience. Start reading today to build your first generation of intelligent agentic systems.