Andrew Ng’s wrote an AI Engineering Skills Map, which lists six things to learn: LLM foundations, grounding models with data, building agentic systems, evaluation-driven development, operating in production, and machine learning foundations.
As he broke it down, I think the most important skill is learning how to build reliable systems from LLM’s uncertain behavior.
You don’t know in advance what an LLM will output
This is the only only truth you need to take away in this post, if you can’t remember all.
You can’t design AI software the way you design normal software — plan it, build it, ship it — because you can’t plan around an output you haven’t seen yet.
The AI engineering stack is packed with jargons now: MCP, CLI tools, sandboxes, memory and context management, harness, loop, RAG, prompt, multi-agent, (sorry, I cannot name them all, too much). But the core of AI engineering is surprisingly simple:
Build something, observe what it does, evaluate whether that’s good enough, change the weakest part, and repeat.