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Context engineering is emerging as the next evolution beyond prompt engineering for AI development. While prompt engineering focuses on crafting perfect inputs, context engineering designs the entire environment where AI agents operate - including memory systems, retrieved data, tools, and persistent state. The approach enables AI agents to make personalized decisions, maintain consistency across sessions, and learn from past interactions. Key context types include system prompts, user inputs, retrieved knowledge (RAG), short-term and long-term memory, available tools, and structured outputs. New frameworks like Memori are being developed to manage these complex memory systems through specialized agents that process, analyze, and retrieve contextual information intelligently.

Context Engineering 101 → From Dumb to Smart AI Agents

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