Another article handwaving or underselling the effects of hallucination. I can't help but draw parallels to layer 2 attempts from crypto.
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On top of that -- rebranding "prompt engineering" as "context engineering" and pretending it's anything different is ignorant at best and destructively dumb at worst.
It is different. There are usually two main parts to the prompt:
1. The context.
2. The instructions.
The context part has to be optimized to be as small as possible, while still including all the necessary information. It can also be compressed via, e.g., LLMLingua.
On the other hand, the instructions part must be optimized to be as detailed as possible, because otherwise the LLM will fill the gaps with possibly undesirable assumptions.
So "context engineering" refers to engineering the context part of the prompt, while "prompt engineering" could refer to either engineering of the whole prompt, or engineering of the instructions part of the prompt.
What I described not only applies to using AI for coding, but to most of the other use cases as well.
> Now I've never found typing to be the bottle neck in software development, even before modern IDEs, so I'm struggling to see where all the lift is meant to be with this tech.
There are many ways to use AI for coding. You could use something like Claude Code for more granular updates, or just copy and paste your entire code base into, e.g., Gemini, and have it oneshot a new feature (though I like to prompt it to make a checklist, and generate step by step).
And that is also not only about just typing, that is also about debugging, refactoring, figuring out how a certain thing works, etc. Nowadays I not only barely write any code by hand, but also most of the debugging, and other miscellaneous tasks I offload to LLMs. They are simply much faster and convenient at connecting all the dots, making sure nothing is missed, etc.