Applied AI: Building with LLMs and Intelligent Agents

Artificial intelligence is shifting from passive chat interfaces to active, autonomous systems that can plan, use tools, and solve complex tasks. Understanding how to work with Large Language Models (LLMs) and orchestrate them into intelligent agents is one of the most critical skills in modern technology. This written course guides you from foundational AI concepts to designing your own functional agent workflows. You will transition from writing simple prompts to understanding how LLMs connect with external data sources, memory systems, and tools to execute multi-step operations. What you'll learn: - Understand the core architecture and capabilities of modern Large Language Models - Master prompt engineering techniques to guide model behavior and output formats - Explore the fundamentals of Retrieval-Augmented Generation (RAG) to connect models with external data - Design autonomous AI agents that can plan, use tools, and execute sequential tasks - Implement basic memory structures to maintain context across agent interactions - Analyze security, reliability, and ethical considerations when deploying AI agents The course begins with essential AI terminology and the mechanics of LLMs before moving into hands-on prompting strategies. From there, you will read through step-by-step implementations of agent architectures, tool integration, and Retrieval-Augmented Generation patterns, all explained through clear conceptual text and code examples. This course is designed for curious beginners, software developers, and tech enthusiasts who want to understand applied AI. No prior background in machine learning is required, though a basic familiarity with programming concepts will help you get the most out of the code snippets. Start reading today to build your foundational knowledge and start designing intelligent AI agents.