Lilly Tech Systems
Learn Context Engineering
Master the art and science of providing the right context to AI models. Learn how to structure, manage, and optimize the information you feed to LLMs for dramatically better results.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is context engineering? Why context matters more than prompt tricks. The context hierarchy explained.
2. Context Windows
Token limits by model, context consumption, window strategies, and cost implications.
3. Context Design
Information architecture for AI, ordering effects, context templates, and dynamic assembly.
4. RAG & Retrieval
Retrieval Augmented Generation explained. Vector databases, embeddings, chunking, and hybrid search.
5. Memory & State
Conversation history, session state, long-term memory, and memory management patterns.
6. Tools & MCP
Function calling, Model Context Protocol, building custom tools, and real-time data integration.
7. Best Practices
Context engineering checklist, common mistakes, caching strategies, security, and team workflows.
What You'll Learn
By the end of this course, you'll be able to:
Design Effective Context
Structure and organize information so AI models can leverage it optimally.
Build RAG Systems
Implement retrieval-augmented generation to ground AI responses in your data.
Manage AI Memory
Implement memory patterns for stateful, personalized AI applications.
Leverage Tools & MCP
Expand AI capabilities with external tools and the Model Context Protocol.