Applied Context Engineering for RAG & LLM Systems
Engineer Reliable LLM Context RAG Memory Agents and Production AI Systems
Created by Paulo Dichone
Master the art of building reliable AI applications by focusing on context engineering, retrieval-augmented generation, and memory systems. Learn how to design, evaluate, and scale LLM-powered workflows for production environments. Tackle real-world challenges through hands-on projects that emphasize resilience, efficiency, and security.
Packt | Sep 2026 | 613 min
What You Will Learn
You will start by understanding how context windows, token usage, and attention mechanisms impact LLM behavior. As you progress, you will design dynamic context pipelines, implement robust RAG systems, and build persistent memory for agents. Each section uses practical projects to reinforce concepts and prepare you for real-world production challenges.
Key Features
- Design and optimize context pipelines to boost LLM reliability and retrieval accuracy
- Build persistent memory and agent workflows for advanced conversational AI systems
- Apply best practices for scaling, securing, and evaluating enterprise-grade AI solutions
Target Audience
Ideal for AI engineers, LLM developers, prompt engineers, data scientists, and technical leads aiming to build dependable AI applications. If you have a basic grasp of AI concepts and want to deepen your skills in context engineering, RAG, and memory systems, you will find this course practical and accessible regardless of your prior experience with these specific technologies.
