Context Engineering represents a significant advancement in AI, moving beyond prompt engineering to encompass a broad understanding of context. Tobi Lutke defines it as the art of supplying complete context for tasks to be effectively handled by LLMs. Agent performance is increasingly determined by the quality of context provided, with many failures now attributable to insufficient context rather than model failure. Effective context consists of system prompts, user prompts, conversational history, long-term memory, relevant retrieved information, available tools, and defined output structures.
Context Engineering is described as the art of providing all the context for the task to be plausibly solvable by the LLM. It plays a crucial role in agent success.
Most agent failures today are attributed to context failures rather than model failures. The quality of the context given to agents is essential for their success.
Understanding context in AI involves more than just the single prompt. It includes instructions, user prompts, conversation history, long-term memory, retrieved information, available tools, and structured output.
The evolution from prompt engineering to context engineering signifies a pivotal shift in AI development, focusing on comprehensive context to enhance interaction quality.
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