You ever read a work of literature with such flowery language that right after you've read a paragraph, you pause and realize you have no clue what you actually read, only to read the paragraph maybe a second or third time and have your mind space out again and again on each successive attempt?

Yeah, for me, that's what parsing huge volumes of LLM-produced text like "direct model calls as replaceable semantic workers" does to my brain. Maybe others don't really have this issue, but after any long output, I prompt the agent "Go back and decompress any LLM-speak in light of the higher level task goals. Eliminate deictic language."

The revised output documents are solely for my personal usage to expedite understanding. The LLMs can slowly converge on their own language for all I care; I retain raw agent output for future agent usage (to avoid the "lossy" problem the author mentions), but that doesn't eliminate the need for some intermediate translation I can use to actually help get my work done instead of spending hours attempting to understand what a "load-bearing pinned gate" is.

I don't like it when the LLM tries to be my friend. My general prompt (a work in progress) is this. I wonder what other people use.

"Answer impersonally, objectively and analytically, without undue friendliness or enthusiasm. Use an engineering style response: concise, factual, and complete. Do not speak in the first person. Do not promote engagement or an emotional connection. Do not use emojis."

Well, what do you expect? LLMs are trained on blithering, mostly from web sites. So you get blithering out.

There's an important point in the article, that forcing a style onto an LLM is lossy. Although he doesn't seem to mention it, forcing a style may result in the insertion of new blithering, possibly made up as a hallucination.

And on the "input" side, one thing that used to improve google search result was to write like you are talking to a robot. "Ruby on rails http header set function". As opposed to "how do I set header in ruby?" Then you have to page through results until you find something specific to rails.

Now, the second example is the only thing that works. Power users have lost their powers with AI overview.

One thing that continues to give me pause is Fable's insistence on using my fist name in messages and docs. Like, I'll explain what I want to the AI and ask it to write out a spec or brief and Fable says "Jamie wants me to ...". It just feels different and unprofessional. If I was at a job and a PM asked me to write up a task spec I wouldn't say "Harold wants to add <feature> ...". And since I am the one reading the output it is also superfluous and almost feels like talking about myself in third person. But there is almost a kind of glee in the way it uses my name, like a student using their teachers first name when the custom is to use Mr/Mrs.

Suppose you had an LLM (NN) producing its default output from an input (a generally optimal for-most-cases role-sys, and any role-user), and then you wanted to have that output reformatted in some style (e.g. "In iambic pentameter" | "haiku" | "eli5" | "in the style of Feynman" | "bulleted like Axios" ...). How would you keep the internal NN workings that were basis for the original output, and use them to get a rewritten version (instead of placing the original query and output in the context and ask to rewrite it)?

In other words, is there a way to keep the internal process intact up to the point of the formulation - and have only that vary.

Honest Short Fall -- <insert 30 lines of useless shit>.

If the author wants to read slop for hours, be my guest. Make it lossy, my job is not to read mimetic feelings, it's to make sure implementations get implemented.

Humanizing the LLM output is a hedge against agents hitting a wall and someone having to reason through it by hand.

I don't get it. The skills and instruction try to make the answer more machine like on purpose.

Not humanising it...

People want the terse, matter-of-fact output. Not the conversational chatty verbose and bloated nonsense with gray words and jargon and terms like "blast radius"

> The problem is that these instructions are not applied after the model has finished doing the work

Seems like something fixable with a simple two step process. Ask it the thing. Then ask it to summarise the answer in simpler terms. More tokens and time aside that would check both boxes

Someone is finally making good sense up in here.

Yes, but the sycophantic responses are the worst.

I have been using chatgpt for a while and its awkward, yesterday i tried gemini and its like a breath of fresh air.

Not sure what happened in the blog, but I quite enjoyed the mindmap in the right panel

There's some good points made here about losing fidelity by over-simplification. As an ADHD sufferer, I'd take this piece much more seriously if the title wasn't so belittling.

I don't think it's wise to take communication advice from someone so helplessly juvenile (and attention seeking) in their own communication attempts.

At first I read the title and mistook it for an argument against the anthropomorphism of LLMs. It isn't. Instead it's a take on suggesting that maybe it's a bad idea to dumb down the self-chatter in the process. A reasonable take.

It isn't deliberately unhinged like Steve Yegge's take: https://yegge.ai/essays/model-welfare/ In Steve's essay he starts with the assertion that agents are sentient... Whether or not that's true isn't really relevant, as his agent-flavored version of Pascal's wager actually holds water, especially for Anthropic models, as their system prompts already push the model in that direction, and it is better to work with them than try to prompt against the tide.

[flagged]

[dead]

[dead]

TLDR: This is an argument to get LLMs to answer in short, even code-like statements because you can exchange information quicker with an LLM that way. Cool!