Context Engineering Masterclass LLMs, RAG & Agents

Master context engineering, RAG pipelines, memory systems & agent design to build production-ready LLM apps
What you'll learn

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Understand how context windows, token economics, and attention limits affect LLM output quality
Design reliable context assembly pipelines that deliver the right info in the right order
Build production-grade RAG systems using chunking, hybrid search, reranking, and compression
Design memory systems for AI apps, including short-term, long-term, and agent memory
Evaluate and improve RAG quality with metrics, context audits, and observability
Engineer context for AI agents using templates, tool outputs, and multi-step state management
Apply production and enterprise patterns for scalable, secure, cost-effective LLM applications
Implement prompt caching strategies that reduce API costs by up to 90%
Create team context standards, versioning workflows, and reusable context libraries
Requirements

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Basic familiarity with LLMs or generative AI tools such as ChatGPT, Claude, or an LLM API is recommended
Some programming experience (Python or JavaScript) helps with the practical implementation examples
No prior RAG or vector database experience is required; all concepts are explained from the ground up
Description

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Context engineering is the most in-demand skill for building reliable AI applications in 2026 and beyond. If you've ever struggled with LLMs hallucinating, ignoring instructions, or losing track of information in long conversations, the problem usually isn't the model - it's the context you're feeding it.
This course teaches you how to systematically design, assemble, and optimize context for large language models, RAG systems, and AI agents so your applications perform consistently in production.
You'll start with the fundamentals of context windows, token economics, and attention dilution, then move into practical, hands-on skills: building production-grade Retrieval-Augmented Generation (RAG) pipelines with chunking, hybrid search, and reranking; designing short-term and long-term memory systems for chatbots and agents; engineering context for multi-step AI agents and tool use; and evaluating context quality with real metrics and observability tools.
By the end of this course, you will be able to
Design context assembly pipelines that reduce hallucinations and improve LLM accuracy
Build and evaluate production-ready RAG systems using modern retrieval and reranking techniques
Implement memory architectures for conversational AI and autonomous agents
Apply enterprise-grade patterns for scalable, secure, and cost-efficient LLM applications
Debug and audit context failures using observability and evaluation frameworks
This course is built for AI engineers, LLM developers, prompt engineers, data scientists, and technical product builders who want to move beyond basic prompting and start engineering context like a systems problem. Whether you're building chatbots, RAG-powered search, or autonomous AI agents, you'll leave with a practical framework you can apply immediately to your own projects.
No prior experience with RAG or vector databases is required - all concepts are explained from first principles, with real code examples and projects throughout. Enroll now and start building AI applications that are accurate, reliable, and production-ready.
Who this course is for

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AI engineers and developers building reliable applications with LLMs, RAG, or AI agents
Prompt engineers who want to move beyond prompts and systematically engineer high-quality context
Data scientists and ML engineers building retrieval, memory, and evaluation layers for AI products
Technical founders and product managers designing production-ready AI features and LLM systems
Team leads standardizing AI development practices across engineering teams
Homepage

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