Apple released a paper showing the diminishing returns of "deep learning" specifically when it comes to math. For example, it has a hard time solving the Tower of Hanoi problem past 6-7 discs, and that's not even giving it the restriction of optimal solutions. The agents they tested would hallucinate steps and couldn't follow simple instructions.
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.
The other is that they intentionally forced LLMs to do the things we know are bad at (following algorithms, tasks that require more context that available, etc) without allowing them to solve it in a way they're optimized to do (write a code that implements the algorithm).
A cynical read is that the paper is the only AI achievement Apple has managed to do in the past few years.
(There is another: they managed not to lose MLX people to Meta)
Context engineering isn’t a rebranding. It’s a widening of scope.
Like how all squares are rectangles, but not all rectangles are squares; prompt engineering is context engineering but context engineering also includes other optimisations that are not prompt engineering.
That all said, I don’t disagree with your overall point regarding the state of AI these days. The industry is full of so much smoke and mirrors these days that it’s really hard to separate the actual novel uses of “AI” vs the bullshit.
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.
good survey of what people are already implementing, but I’ve convinced we barely understand the possibility space here. There may be much more elaborate structures that we will put context into that haven’t been discovered yet
Nah ! I am not convinced that context engineering is better (in the long trem) than prompt engineering. Context engineering is still complex and needs maintainance. Its much lower level than human level language.
Given that domain expertise of the problem statment, we can apply the same tactics in context engineering on higher level in prompt engineering.
This whole industry is complex and needs constant maintenance. APIs break all the time -- and that's assuming they were even correct to begin with. New models are constantly released, each with their own new quirks. People are still figuring out how to build this tech -- and as quickly as they figure one thing out, the goal posts move again.
This entire field is basically being built on quicksand. And it will stay like this until the bubble bursts.
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.
The other is that they intentionally forced LLMs to do the things we know are bad at (following algorithms, tasks that require more context that available, etc) without allowing them to solve it in a way they're optimized to do (write a code that implements the algorithm).
A cynical read is that the paper is the only AI achievement Apple has managed to do in the past few years.
(There is another: they managed not to lose MLX people to Meta)
Like how all squares are rectangles, but not all rectangles are squares; prompt engineering is context engineering but context engineering also includes other optimisations that are not prompt engineering.
That all said, I don’t disagree with your overall point regarding the state of AI these days. The industry is full of so much smoke and mirrors these days that it’s really hard to separate the actual novel uses of “AI” vs the bullshit.
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.
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.
Given that domain expertise of the problem statment, we can apply the same tactics in context engineering on higher level in prompt engineering.
This entire field is basically being built on quicksand. And it will stay like this until the bubble bursts.