Context Engineering is an emerging concept in AI, evolving from “prompt engineering” to encompass a broader scope. Tobi Lutke defines it as “the art of providing all the context for the task to be plausibly solvable by the LLM.” The success of AI agents increasingly depends on the quality of context provided, with many failures attributed to context rather than model limitations. Understanding context involves various components, including instructions, user prompts, short-term and long-term memory, retrieved information, available tools, and structured output. The transition from basic to advanced AI agents isn’t about coding complexity but rather about delivering rich context for effective responses. For example, a well-contextualized agent integrates calendar data and previous interactions to generate meaningful replies, whereas a poorly contextualized agent offers generic responses. Thus, Context Engineering is fundamental for providing the right information and tools at the right time, facilitating the accomplishment of tasks by LLMs.
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