@gifamossAndrew Ng mapped out four essential skills for developers in AI engineering, and a two-page reference sheet based on that framework explains how to build and deploy AI applications that can be measured, evaluated, and improved. The document details why software engineering fundamentals still matter when coding agents write most of the implementation, plus it describes using coding agents without losing control over context, architecture, testing, security, or production data. There is a shift in the engineer's role from simply writing code to deciding what should be built. The resource distinguishes between an AI demo and a reliable AI system while covering the evaluation and error-analysis loop. It lists tradeoffs every coding agent needs help with, discusses the role of product sense and customer context, and offers a practical way to find which skill is missing in your next project alongside a reusable checklist for reviewing an AI build. AI engineering involves shaping systems, providing context to agents, verifying work, and improving results through evidence rather than just generating code. This compact reference comes from AndrewYNG.
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