Context engineering is emerging as a critical skill for AI engineers, focusing on systematically orchestrating context rather than just clever prompting. Unlike traditional prompt engineering that relies on 'magic words', context engineering creates dynamic systems that provide the right information, tools, and format to LLMs. The approach addresses the real bottleneck in AI applications - not model capability, but setting up proper information architecture. Key components include dynamic information flow, smart tool access, memory management (both short-term and long-term), and format optimization. As AI models improve, context quality becomes the limiting factor for application success.

4m read timeFrom blog.dailydoseofds.com
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Announcement: We are hiring (fully remote roles)!An intuitive guide to context engineeringP.S. For those wanting to develop “Industry ML” expertise:

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