Context engineering is the discipline of deliberately designing everything a large language model sees before it generates a response, not just the instruction you type, but the retrieved documents, conversation history, tool definitions, and memory that surround it. Where a prompt is a single instruction, context is the entire informational environment the model uses to act on that instruction.<\/p>\n\n\n\n
The term crystallised in mid-2025 and has since become the dominant way serious AI builders describe the actual work of making LLM applications reliable. Context engineering treats the context window, the model’s limited working memory, as something to be architected with the same rigor engineers apply to database schema design or API contracts, rather than something you fill in ad hoc with whatever seems relevant.<\/p>\n\n\n\n
Context engineering was popularised in June 2025 by Andrej Karpathy, the AI researcher and former OpenAI founding member, who argued that ‘prompt engineering’ understated the real work involved in building serious LLM applications.<\/p>\n\n\n\n
Shopify CEO Tobi L\u00fctke independently echoed the same framing around the same time, describing context engineering as the art of providing all the context needed for a task to be plausibly solvable by the model. Karpathy’s own explanation uses a computer-architecture analogy that’s become widely cited: the LLM is the CPU, the context window is RAM, and context engineering is the operating system deciding what actually gets loaded into that limited working memory.<\/p>\n\n\n\n
The distinction between context engineering and being a prompt engineer is not just semantic, the two disciplines answer fundamentally different questions.<\/p>\n\n\n\n\n