The intentional design and management of the linguistic, informational, and interactional context in which an AI system operates in order to influence its outputs over time. Unlike prompt engineering, which focuses on individual prompts, context engineering shapes the broader conditions that guide model behavior—such as persistent instructions, accumulated examples, role continuity, reference materials, and conversational history—leveraging how language models extend patterns across context windows.
Context engineering reflects a shift from controlling AI through phrasing to guiding it through structured environments, making it especially relevant for learning systems, knowledge platforms, and workplace AI tools.
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