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上下文就是一切!行业热议话题:提示工程是否应该改名
歸藏的AI工具箱· 2025-06-26 11:40
Core Viewpoint - The article discusses the emerging concept of "context engineering" in AI, suggesting it is a more accurate term than "prompt engineering" to describe the skills needed for effectively utilizing large language models (LLMs) [1][2]. Group 1: Importance of Context Engineering - Context engineering is essential for optimizing the performance of AI agents, as insufficient context can lead to inconsistent actions among sub-agents and hinder the ability to follow instructions accurately [4][5]. - The performance of LLMs can decline if the context is too long or contains irrelevant information, which can also increase costs and delays [4][5]. - Instruction adherence is crucial for agents, with top models showing a significant drop in accuracy during multi-turn conversations, highlighting the need for optimized context length and accuracy [4][5]. Group 2: Strategies for Optimizing Context Engineering - Context engineering encompasses three common strategies: compression, persistence, and isolation [5][6]. - Compression aims to retain only the most valuable tokens in each interaction, with methods like context summarization being critical [6][7]. - Persistence involves creating systems for storing, saving, and retrieving context over time, considering storage methods, saving strategies, and retrieval processes [9][10]. - Isolation focuses on managing context across different agents or environments, utilizing structured runtime states to control what LLMs see in each interaction [16][18]. Group 3: Practical Experiences and Recommendations - The article emphasizes the importance of building robust context management systems for AI agents, balancing performance, cost, and accuracy [24]. - It suggests that memory systems should be simple and track specific agent preferences over time, while also considering parallelizable tasks for multi-agent architectures [26]. - The need for a token tracking mechanism is highlighted as foundational for any context engineering work [23].