Foundations of Context | LLM Context Engineering Bootcamp | Lecture 1

Want to go beyond just watching? Enroll in the Engineer Plan or Industry Professional Plan at https://context-engineering.vizuara.ai to get access to all Google Colab notebooks, interactive web exercises, private Discord community, Miro boards, a private GitHub repo with all code, and the capstone build sessions where you build a production-grade AI agent alongside the instructors. These plans give you hands-on materials for every session and direct support from the teaching team — everything you need to actually implement what you learn, not just watch it. Enroll now: https://context-engineering.vizuara.ai In this first session of the AI Context Engineering Bootcamp, Dr. Sreedath Panat introduces context engineering — the discipline that separates AI demos from production-grade systems. We start with a real-world motivation: the challenge of building an email reply agent that can respond as accurately and naturally as a human, and why simply prompting an LLM falls short. This leads into the core idea of the session — prompt engineering only optimizes the user input, which is just 1 of the 6 elements that make up an LLM's context. Context engineering designs all 6. We walk through the evolution of how developers interact with AI — from basic prompting in 2023, to vibe coding in early 2025, to context engineering in mid-2025, and now to agentic engineering in 2026. Three leaders — Andrej Karpathy, Tobi Lutke (Shopify), and Harrison Chase (LangChain) — independently arrived at the same term in the same week, and we compare their definitions side by side. The session introduces Karpathy's LLM OS analogy, where the LLM model maps to a CPU, the context window to RAM, RAG and vector databases to the file system, tools and MCP to system calls, and the system prompt to the OS kernel. This mental model becomes the foundation for the entire bootcamp. We then break down the six elements of context — system instructions, user input, conversation history, retrieved knowledge, tool definitions, and state/memory — and show how each one contributes to output quality. We cover the economics of context: how to calculate that a 2-million-token context window fits roughly 5,000 pages, why stuffing it full actually degrades performance through context rot, and the "Lost in the Middle" effect from the 2023 research paper showing that LLMs pay significantly less attention to information placed in the middle of the context. We also discuss a recent Google paper showing that simply repeating the prompt twice can substantially improve output quality. The hands-on exercise is the highlight. Using an interactive web notebook (no coding experience required) and Google Colab, we progressively build context for a customer support chatbot — starting with just the user input, then adding system instructions, retrieved knowledge from RAG, tool definitions, conversation history, and finally state and memory. At each step, a judge LLM scores the response quality, and we watch the scores climb from around 18/40 with bare user input to over 30/40 with all six layers. The session wraps up with a 15-question quiz to reinforce the key concepts. This bootcamp covers 8 core sessions — Context Foundations, System Prompts, RAG Pipelines, MCP and Tools, Compress and Isolate, Memory Architectures, Production Patterns, and a Capstone Build Project — plus 2 mega build sessions. #ContextEngineering #LLM #AIBootcamp #Vizuara