A few of these points are right, but a lot of it is so far lost from the reality of LLMs that it isn't even wrong.
Yes: structure beats vibes. Primacy/recency bias is real. Treating prompts as engineering artifacts is empirically helpful.
But:
- “Reads all tokens at once.” Not quite. Decoder LLMs do a parallel prefill over the prompt, then sequential token-by-token decode with causal masks. That nuance is why primacy/recency and KV-cache behavior matter, and why instruction position can swing results.
- Embeddings & “labels.” Embeddings are learned via self-supervised next-token prediction, not from a labeled thesaurus. “Feline ≈ cat” emerges statistically, not by annotation.
- "Structure >> content". Content is what actually matters. “Well-scaffolded wrong spec” will give you confidently wrong output.
- Personas are mostly style. Yes, users like words typed in their style better, but it'll actually hide certain information that a "senior software engineer" might not know.
I don't really get the Big-O analogy thing, either. Models are constantly exposing and shifting how they direct attention, which is exactly the opposite of the durably true nature of algorithmic complexity. Memorizing how current models like their system prompts written is hardly the same thing.
I agree with your opinion. This statement you called out, “Reads all tokens at once.”, shows the misunderstanding, and then the document header formatting shows/proves the lack of attention used.
The headers go from numbered to having just 1 section with a sub-number. Section 3 has 3.1-3.4, and then the next section doesn't follow it.
I noticed this when doing large-scale LaTeX documentation builds if you are not explicit about the formatting. The well-scaffolded build falls apart since the token count is too high, and a proper batch process is not in place, or the prompt is vague. "Use clear headings and bullet points" is not precise. Depending on your document type, you need to state all requirements to design with attention.
I don't buy the arguments made here. You can't call it attention optimized without opening the LLM brain and assessing what happens in the attention layers. Does the quoted paper did some of that? I know Anthropic is advanced in this area but I haven't seen that many results elsewhere yet. I mean the fact that optimizing for attention makes better prompt is a solid hypothesis, but I don't read a proof here.
I think the author is confusing the attention mechanism with attention in the general sense. He refers to a phenomenon called “attention drift”, but neither Google nor LLM searches return any relevant references to that term. In fact, ChatGPT only cited this same blog post.
I think LLM vendors papers entertain such confusions, the lack of peer reviewing tends to favour grandiose but barely defined wording as such. At least author seems to have checked a few papers that do some empirical validation (which is still different from establishing any kind of theory of prompt engineering, there is no other way than cutting the model up to do that).
Yea they sound like plausible enough arguments.. I often test my vague input against structured ones and for many tasks it didn't seem like hallucinations happened significantly less. Honestly the more structured you have to be, the less "general" your model probably is, and in theory you want your model to be as general as possible. Seems like here, you're simply helping it over-fit.. At least that's what my intuition tells me but I am yet to really check that out either.
Not sure if this is a part of it, but there are places where "engineer" is a protected title that requires a degree or license. I work with a bunch of folks in Quebec and they have to use the title "software developer" unless they are a member of the Order of Engineers. I find this to be pretty silly considering someone can have a degree in Mechanical Engineering and use the title "Software Engineer" but someone with a degree in Computer Science can't.
The less rigorous and more vague some theory is, the easier it is to use it to make unfalsifiable claims. That's the essence of the current discussion around AGI. No one knows what it is or describe it concretely so it can do anything and everything and everyone's going to lose their jobs.
It's funny because of the irony of "prompt engineering" being as close to cargo culting as things get. No one knows what the model is or how it's structured in a higher level (non implementation) sense, people just try different things until something works, and try what they've seen other people do.
This article is at least interesting in that it takes a stab at explaining prompt efficacy with some sort of concrete basis, even if it lacks rigor.
It's actually a really important question about LLMs: how are they to be used to get the best results? All the work seems to be on the back end, but the front end is exceedingly important. Right now it's some version of Deep Thought spitting out the answer '42'.
The LLM is basically a runtime that needs optimized input bc the output is compute bottlenecked. Input quality scales with domain knowledge, specificity and therefore human time input. You can absolutely navigate an LLMs attention piecemeal around a spec until you build an optimized input.
FWIW, after my programming skills became hopelessly outdated, with an economics degree and too old to start over, I generally promoted my skillset as a translator between business and tech teams.
A lot of what I received as input was more like the first type of instruction, what I sent to the actual development team was closer to the second.
To add to these good points - for bigger changes, don't just have LLMs restructure your prompt, but break it down into a TODO list, a summary and/or build a scaffolded result then continue from that. LLMs thrive in structure, and the more architectural you can make both your inputs and outputs, the more consistent your results will be.
For example, pass an LLM a JSON structure with keys but no values and it tends to do a much better job populating the values than trying to fully generate complex data from a prompt alone. Then you can take that populated JSON to do something even more complex in a second prompt.
Does Claude code do this by default?
It seems for most prompts that I give it, it ends up breaking things into TODO lists and reformulating the prompt. This seems to work well for most tasks.
I'll admit that it's a little shoehorned in at the end :)
... but you know how editors are with writing the headline for clicks against the wishes of the journalist writing the article. You'll always see Journos sayign stuff like "don't blame me, that's my editor, I don't write the headlines"
I did toy with the idea of going with something like:
`Prompt Engineering is a wrapper around Attention.`
There’s a repeated paragraph in it atm you may want to fix:
“This is why prompt structure matters so much. The model isn’t reading your words like you do — it’s calculating relationships between all of them simultaneously. Where you place information, how you group concepts, and what comes first or last all influence which relationships get stronger weights.
This is why prompt structure matters so much. The model isn’t reading your words like you do — it’s calculating relationships between all of them simultaneously”
Reprimand the editor. ;)
I look forward to using the ideas in this, but would be much more excited if you could benchmark these concepts somehow, and provide regular updates about how to optimize.
"The key difference isn’t the words — it’s how you structure the thinking process. By breaking down the task into numbered steps as we see in option B, you’re leveraging how transformer attention works: structured, sequential instructions create clearer context that guides the model’s reasoning."
Can you support that assertion in a more rigorous way than "when I do that I seem to get better results?"
TBH I almost passed this article by because of the big-o reference - it seemed like such a strange thing to say.
I'm glad I didn't, though, because I had no idea how the LLM is actually interpreting my commands, and have been frustrated as a result.
Maybe a title like "How to write LLM prompts with the greatest impact", or "Why your LLM is misinterpreting what you say", or something along those lines.
This was a great refresher on things I’ve seen writings of but never thought deeply about. A lot of it already “made sense” yet in my day to day I’m still doing the bad versions of the prompts.
Do you have preference n a more continual system like Claude Code for one big prompt or just trying to do one task and starting something new?
Having a lot of context in the main agent is an antipattern. You want the main agent to call subagents to keep high level reasoning context clean. If you're not using subagents you should start a new chat any time you're doing something fairly different from what you were doing before.
Readit News
Yes: structure beats vibes. Primacy/recency bias is real. Treating prompts as engineering artifacts is empirically helpful.
But:
- “Reads all tokens at once.” Not quite. Decoder LLMs do a parallel prefill over the prompt, then sequential token-by-token decode with causal masks. That nuance is why primacy/recency and KV-cache behavior matter, and why instruction position can swing results.
- Embeddings & “labels.” Embeddings are learned via self-supervised next-token prediction, not from a labeled thesaurus. “Feline ≈ cat” emerges statistically, not by annotation.
- "Structure >> content". Content is what actually matters. “Well-scaffolded wrong spec” will give you confidently wrong output.
- Personas are mostly style. Yes, users like words typed in their style better, but it'll actually hide certain information that a "senior software engineer" might not know.
I don't really get the Big-O analogy thing, either. Models are constantly exposing and shifting how they direct attention, which is exactly the opposite of the durably true nature of algorithmic complexity. Memorizing how current models like their system prompts written is hardly the same thing.
The headers go from numbered to having just 1 section with a sub-number. Section 3 has 3.1-3.4, and then the next section doesn't follow it.
I noticed this when doing large-scale LaTeX documentation builds if you are not explicit about the formatting. The well-scaffolded build falls apart since the token count is too high, and a proper batch process is not in place, or the prompt is vague. "Use clear headings and bullet points" is not precise. Depending on your document type, you need to state all requirements to design with attention.
It's funny because of the irony of "prompt engineering" being as close to cargo culting as things get. No one knows what the model is or how it's structured in a higher level (non implementation) sense, people just try different things until something works, and try what they've seen other people do.
This article is at least interesting in that it takes a stab at explaining prompt efficacy with some sort of concrete basis, even if it lacks rigor.
It's actually a really important question about LLMs: how are they to be used to get the best results? All the work seems to be on the back end, but the front end is exceedingly important. Right now it's some version of Deep Thought spitting out the answer '42'.
> 1. the branch of science and technology concerned with the design, building, and use of engines, machines, and structures.
2. the action of working artfully to bring something about.
So you're trying to learn what / how to prompt, in order to "bring something about" (the results you're after).
-A Prompt Architect
Seems like an LLM should be able to judge a prompt, and collaboratively work with the user to improve it if necessary.
https://www.dbreunig.com/2025/06/10/let-the-model-write-the-... is an example.
You can see the hands on results in this hugging face branch I was messing around in:
here is where I tell the LLM to generate prompts for me based on research so far
https://github.com/AlexChesser/transformers/blob/personal/vi...
here is the prompts that produced:
https://github.com/AlexChesser/transformers/tree/personal/vi...
and here is the result of those prompts:
https://github.com/AlexChesser/transformers/tree/personal/vi.... (also look at the diagram folders etc..)
Write your prompt in some shape and ask grok
Please rewrite this prompt for higher accuracy
-- Your prompt
How do you know it won't introduce misinformation about white genocide into your prompt?
A lot of what I received as input was more like the first type of instruction, what I sent to the actual development team was closer to the second.
For example, pass an LLM a JSON structure with keys but no values and it tends to do a much better job populating the values than trying to fully generate complex data from a prompt alone. Then you can take that populated JSON to do something even more complex in a second prompt.
I found the “Big-O” analogy a bit strained & distracting, but still a good read.
... but you know how editors are with writing the headline for clicks against the wishes of the journalist writing the article. You'll always see Journos sayign stuff like "don't blame me, that's my editor, I don't write the headlines"
I did toy with the idea of going with something like: `Prompt Engineering is a wrapper around Attention.`
But my editor overruled me *FOR THE CLICKS!!!*
Full disclosure: I'm also the editor
“This is why prompt structure matters so much. The model isn’t reading your words like you do — it’s calculating relationships between all of them simultaneously. Where you place information, how you group concepts, and what comes first or last all influence which relationships get stronger weights. This is why prompt structure matters so much. The model isn’t reading your words like you do — it’s calculating relationships between all of them simultaneously”
Reprimand the editor. ;)
I look forward to using the ideas in this, but would be much more excited if you could benchmark these concepts somehow, and provide regular updates about how to optimize.
Can you support that assertion in a more rigorous way than "when I do that I seem to get better results?"
I'm glad I didn't, though, because I had no idea how the LLM is actually interpreting my commands, and have been frustrated as a result.
Maybe a title like "How to write LLM prompts with the greatest impact", or "Why your LLM is misinterpreting what you say", or something along those lines.
Do you have preference n a more continual system like Claude Code for one big prompt or just trying to do one task and starting something new?