Context Engineering is a comprehensive course designed to help you master the stateless nature of LLMs and understand how context enables seamless multi-turn conversations from the ground up.

You will learn how to distinguish Context Engineering from basic prompt engineering by focusing on the core principle of prioritizing "Better Context" over "More Context". The curriculum covers how to manage context window limits and token costs while eliminating critical issues like context rot, attention dilution, and the "lost-in-the-middle" phenomenon.

Starting from the seven core elements of modern AI context, the course gradually moves into structuring LLM inputs and optimizing performance using KV Caching, exploring self-attention, Q/K/V matrices, chat templates, and implicit versus explicit caching. You will also dive deep into Retrieval-Augmented Generation (RAG), mastering markdown-aware parsing, dynamic chunking, contextual retrieval, and vector storage with ChromaDB to solve the amnesia problem.

You will explore advanced memory architectures, covering the three pillars of long-term memory (semantic, episodic, and procedural), memory injection strategies, and engineering custom memory with temporal conflict resolution. Additionally, the course tackles context compression across the four drivers of cost, latency, quality, and the hard wall, teaching both lossless techniques like JSON-to-CSV minification and lossy summarization pipelines.

A major focus of the course is Tool Calling and the Model Context Protocol (MCP), where you will learn to handle deferred tool loading, programmatic tool calling, and solve context bloat caused by tool definitions and results. You will understand MCP architecture, including hosts and servers, and how to implement a filtration layer for tools and textual data.

By the end of the course, you will be equipped to build highly optimized applications by managing context accumulation effectively at both the AI system and LLM ends.

Course Duration: 25+ hours
Refund Policy: 4 days