Context engineering is the continual struggle of software engineers to explain themselves, in an industry composed of weak communicators that interrupt to argue before statements are complete, do not listen because they want to speak, and speak over one another. "How to use LLMs" is going to be argued forever simply because those arguing are simultaneously not listening.
I really don’t think that’s a charitable interpretation.
One thing I’ve noticed about this AI bubble is just how much people are sharing and comparing notes. So I don’t think the issue is people being too arrogant (or whatever label you’d prefer to use) to agree on a way to use.
From what I’ve seen, the problem is more technical in nature. People have built this insanely advanced thing (LLMs) and now trying to hammer this square peg into a round hole.
The problem is that LLMs are an incredibly big breakthrough, but they’re still incredibly dumb technology in most ways. So 99% of the applications that people use it for are just a layering of hacks.
With an API, there’s generally only one way to call it. With a stick of RAM, there’s generally only one way to use it. But to make RAM and APIs useful, you need to call upon a whole plethora of other technologies too. With LLMs, it’s just hacks on top of hacks. And because it seemingly works, people move on before they question whether this hack will still work in a months time. Or a years time. Or a decade later. Because who cares when the technology would already be old next week anyway.
It's not a charitable opinion. It is not people being arrogant either. It's the software industry's members were not taught how to effectively communicate, and due to that the attempts by members of the industry to explain create arguments and confusion. We have people making declarations, with very little acknowledgement of prior declarations.
LLMs are extremely subtle, they are intellectual chameleons, which is enough to break many a person's brain. They respond as one prompts them in a reflection of how they were prompted, which is so subtle it is lost on the majority. The key to them is approaching them as statistical language constructs with mirroring behavior as the mechanism they use to generate their replies.
I am very successful with them, yet my techniques seem to trigger endless debate. I treat LLMs as method actors and they respond in character and with their expected skills and knowledge. Yet when I describe how I do this, I get unwanted emotional debate, as if I'm somehow insulting others through my methods.
