I learned about context engineering for you, so you don’t have to spend hours doomscrolling X and Reddit trying to piece together what it even means. In this video, I break down a clean definition: context engineering is designing and building dynamic systems that give a language model the right info, in the right format, at the right time—aka packing the context window correctly. And the big nuance is this: context engineering is mainly relevant if you’re building LLM apps (especially AI agents). If you’re just chatting with ChatGPT about running shoes, that’s still prompt engineering, and it’s still useful. Then I show what context engineering looks like in practice for agents. I walk through the core “burger components” of an AI agent—model, tools, knowledge/memory, audio/speech, guardrails, and orchestration—and explain that the context engineer’s job is basically writing the instruction manual for how all of that fits together. Finally, I demo a structured system prompt for my own AI research assistant, using markdown sections and XML tags, with step-by-step requirements and a strict JSON output format. The takeaway: as agents get real (not just demos), your “prompt” starts looking like code—and getting the context right becomes the whole game.