Context Engineering: Managing Memory and Tools in AI Applications

As AI applications evolve, writing simple prompts is no longer enough to build truly intelligent systems. To create reliable AI agents and assistants, developers must master context engineering—the art of managing model memory, token windows, and external tool integration.\n\nThis course guides you from the fundamental principles of AI context to implementing sophisticated memory patterns. You will transition from basic prompting to structuring rich context environments, enabling your AI systems to remember past interactions and fetch real-time data autonomously.\n\nWhat you'll learn:\n- Understand the core mechanics of context windows, token limits, and system instructions.\n- Design stateful AI applications using conversation history and memory management strategies.\n- Integrate external APIs and databases using modern function-calling and tool-use patterns.\n- Implement Retrieval-Augmented Generation (RAG) concepts to connect models with external knowledge.\n- Optimize context efficiency to reduce latency and lower computational costs.\n- Practice structuring complex context payloads through step-by-step written exercises.\n\nYou will start by exploring foundational AI terminology and how models process inputs, before moving on to practical techniques for managing memory and integrating tools.\n\nThis course is designed for developers, aspiring prompt engineers, and tech-savvy beginners who want to go beyond simple prompts. No prior experience with advanced AI engineering is required.\n\nStart reading today to unlock the full potential of context-aware AI systems.