I personally think context and prompt engineering are not useful things to learn. If you’re having trouble improving the reliability and maintainability of your systems, the solution is to learn how to design better systems. And that’s great news for you, because there’s a ton of resources out there to learn about systems design! That’s normal engineering, and it doesn’t change as quickly as AI engineering… which, full disclosure as someone who uses a lot of token every day, is overhyped anyway.

AI can help tremendously but there’s so much you as a human understand about product direction and real-world use that the AI simply can’t. Everywhere you look, companies are popping up that are desperate to ingest data from every possible source in order to fix this context and intuition gap. But it’s just not possible. As long as you’re the one who ultimately decides what features you’ll implement, the model will be bad at guessing what to build for in the future. And anyway, you can spend all your energy learning how to squeeze the last 10% of work out of your model and harness and workflow but at the end of the day that’s time you’re not learning about designing resilient systems and actually doing the hard work of implementing them.

Bias: I’m a software engineer, and not super ai-pilled, so. There’s that.

The AI improves and that is some other entity's job.

Should you have expertise in your domain with strong fundamentals and capacity to envision a world's best prototype to make, as measured, knowing conceptually how the model reduces your inputs to the desired output is enough.

Experts do get frustrated when the AI quits short on their prompt.

SOTA example has senior engineer one shot flashcard vocab building app for language learning.

Find a bootcamp, the fundamentals don;t change or get stale.

My advice is to learn the fundamentals of software engineering. Take some good books or resources, learn what makes a practice good or bad and implement it via projects.

Some ideas on what you should learn based on what you said:

Data modeling or database design: this is probably where AI-generated apps rot fastest as they grow. Learn normalization, indexing, and why schema choices matter.

Security basics: auth, input sanitization, secrets management, common vuln classes (OWASP top 10 is a fine starting ground).

Testing philosophy: not necessarily syntax, but why you test, what to test, and how tests let you (and the AI) refactor safely. This is probably the single most important thing for someone building with AI, since its a safety net for code you didnt write line-by-line.

System design and architecture basics: how to break something into services/modules, when not to, tradeoffs of monoliths vs. microservices at small scale, stuff like that.

I would really recommend you to learn syntax, however the bare minimum is to be able to read the code and actually understand it. For example, knowing when a function is doing too much.