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Claude's "honest" is an interesting example because we can trace it to a specific document that it was trained on extensively: the "Constitution" is identified to Claude in its training as the core of what it is, and it uses the word "honest" or a derivative 57 times, including having a whole section on it.

> Honesty is a core aspect of our vision for Claude’s ethical character. Indeed, while we want Claude’s honesty to be tactful, graceful, and infused with deep care for the interests of all stakeholders, we also want Claude to hold standards of honesty that are substantially higher than the ones at stake in many standard visions of human ethics.

https://www.anthropic.com/constitution

I asked it to remove "honest" from a draft once.

"Why say honest? We're talking to our coworkers. We would always be honest."

I'm going to look for prompts or skills that can train it in technical writing but I'm warning the AI enthusiasts in my company that its first drafts of code and prose are low-quality, you have to hold it to a high standard yourself.

I actually took a single technical writing class in college so I might be the only one who remembers "Omit needless words."

> LLMs seem to get tragically stuck on certain patterns.

That is likely an artifact of the fine-tuning process:

> Once a style tic is rewarded, later training can spread or reinforce it elsewhere, especially if those outputs are reused in supervised fine-tuning or preference data.

> That creates a feedback loop:

> * Some rewarded examples contain a distinctive lexical tic.

> * The tic appears more often in rollouts.

> * Model-generated rollouts are used for supervised fine-tuning (SFT).

> * The model gets even more comfortable producing the tic.

https://openai.com/index/where-the-goblins-came-from/

I'd suggest "Caveat".

The problem

While an article lends a headline more weight, in incomplete phrases consisting solely of a substantive, "The" is a superfluous rhetorical device.

"The Exorcist" could just as well be named

"Exorcist".

But it was not the style at the time.

We already know it's important. If The Caveat doesn't stand out enough without The, maybe one should consider interleaving it with the preceding text, or increasing the heading level.

Do you want me to increase the heading level of Caveat by using only a single #?

But hear me out: there comes

# The Markdown Trap

In fact, this is not always possible, because heading levels decrease when adding # characters, which limits our headroom.

## The solution

I've implemented a Markdown transpiler that assigns inverted heading levels based on the number of #s.

With # beinh regular body font size, mapped to ######.

Higher heading levels are compiled to style attributes, providing an almost limitless signifikance scale and infinite nesting levels.

So from now on, you can use

  # Heading 

Work your way up to

  ###### The Caveat

And more hash signs make it stand out even more.

(green checkmark)

markdown-transpiler.sh

The ones that strike me are the ones exaggerating certitude, to an inappropriate degree and with a certain degree of excitement:

“Exact” “Honest” “Load-bearing” “Root cause”

I know there are more that are slipping my addled mind. But what stands out to me is a sense of a junior who’s very proud that they’ve conquered the murk and messiness and achieved True Certitude in their pursuit of their task. Compensating, with emphatic tone and bravado, for the uneasy feelings and self-doubt of battling chaos with the tools of reason.

…Even as it’s usually my job to let them down gently as I puncture their tidy analysis and reintroduce complications… you want a root cause analysis, Claude old boy, let’s make a root cause analysis…

I also noticed Gemini's habit of getting stuck on things I said. It became evident quite quickly. I haven't noticed this in the same way in any other model. Something's wrong with that boy

you should be careful about the times it doesn't say honest!

A fun example, always shake my head when I read it again: https://openai.com/index/where-the-goblins-came-from/

My honest opinion is that Claude's overuse of "honest" really damages the quality of its rhetoric. Why wouldn't you be honest? Were you lying before? Why even invite the question?

Claude is overall incredibly useful as a writing assistant. It can come up with words and phrases that make a point so much clearer than I am capable of doing - but for every improvement, there's about a dozen silly LLM-isms that I have to filter manually. It's one of the things that might define the boundary between LLM intelligence and human intelligence well into the future - the art of rhetoric is extremely context-sensitive, and the current generation of models can't help but take a one-size-fits-all approach.

Interestingly this also happens between humans with frequent communication, it is called linguistic convergence.

We are changing LLMs text patterns while it is changing the way we write and speak.

https://www.axios.com/2026/05/02/ai-changing-writing-speakin...

use the wrong phrasing and suddenly you can create your own word of the day for an AI model.

I have a delightful time poisoning my company's AI system this way.

I invented my own word that sounds perfectly cromulent† to an ordinary person, and any brain that's read a book learns how to infer meaning from context, so it's not a problem.

When I get a e-mail response from a coworker using my special word incorrectly, then I know it's AI and I respond telling the coworker I don't know what that word means. Busted.

† It's not actual "cromulent," but any Simpsons fan or human brain will know what I mean.

> "You're absolutely right to question me, father — in fact, I did eat all the cookies. But it's not a load-bearing issue — my honest take is simple: we go to the store and buy more."

FTFY

ah, didnt know this! Thanks for sharing :)

They can't, because they use RL with synthetic data and LLMs as judges. So the system naturally convergences towards certain load bearing, genuine, not just annoying but ridiculous verbal tics.

It's probably the reason most LLMs share the same tics across labs, because they cross train and distil each other's models on an industrial scale. You also can't escape it in generated text that's already online. So if, say ChatGPT first had some random idiosyncrasies, it then contaminated the entire AI ecosystem.

Yes that would probably help!

Or tech companies could stop staring at their own belly-buttons and realize there's a whole big world outside of Silicon Valley, and training on the writing styles and pattens of their bubble and its hangers-on is perhaps not all that useful outside of 415.

Apple used to be guilty of this back when you'd ask Siri what the temperature was, and any number above 79°F was followed by the word "Hot!"

This sort of take is so tired and boring, and frankly has zero grounding in reality.

"LLMs will never <X>" is constantly being disproven every time they scale up to the next 10X and apply architectural improvements.

Their internal representations are so cryptic and complex that even the top AI researchers don't really know how they work or what their limits are. No one is going to take you seriously as a rando HN user if you're claiming to know better than them.

Humans easily anthropomorphize things that are not humans, ascribing human attributes like motive and comprehension and emotion to objects and processes that are not people who can have those attributes.

Claude is not a human.

It is overwhelmingly easier to anthropomorphize Claude or Siri or an LLM that communicates with you more eloquently than your boss than it is to anthropomorphize a cranky, tired starter motor. It's often easier to do than it is not to do, and sometimes, it's a useful abstraction. But it's not precise or correct, and can result in errors.

It could also just be that they're getting confused when using tools configured without a username dedicated to the tool. It's easy to end up with a comment or commit message that says "I prefer X over Y" posted on Alxndr's account and have coworkers confused whether that's the LLM or the human making that statement.

IIRC I experienced this confusion the most when reading commit messages and documentation authored by Claude in my repos. Now that I've managed to convince it to stop using first-person pronouns, I haven't gotten tripped up by its prose.

I think a second-order effect is that my installation of Claude writes with a less-personal perspective, which I'm also finding a little easier to understand.

This is a method of manipulating the LLM, it doesn't have to be true.

I've given LLMs religion before to manipulate their behavior, that doesn't mean I believed in the great spaghetti goddess.

Are there any skills or publicly available repos that do what you claim here? I would love to learn to have it write better.

For me, my amateur attempt is having another LLM do a review loop to remove clearly offending phrases and a heuristic eval to change sentence structures to be more similar to mine, THEN my manual HITL loop to rewrite ~20% of the sentences anyway.

What do you suggest for articulating the writing style that one wants from LLMs?

I’ve been experimenting with having LLMs write/update academic notebooks for me, and so far the best results I’ve gotten came from correcting their output and asking them what they’ve “learned” from my feedback.

Mine is obsessed with "planes". Data plane, control plane, management plane. Everything is a plane :)

I’ve heard (and used) the term “API surface” a lot…

In my brief and abortive foray into education, I discovered that they friggin' love to use "surface" as a verb. As in: This activity surfaces an understanding of the turboencabulation principle for learners. Or somesuch. It's been a while, happily.

Unless you're a submarine, "surface" is not a verb.

It's pretty common to read "attack surface" in security.

Recently read some LLM generated output that mentioned the “center of gravity” within a codebase.

Also have read the term “seam” dozens of times by now, when previously I saw it maybe once or twice over years. Very abstract term.

That one probably comes from maths, where surfaces show up all the time in geometric interpretations of things. I've been involved in more mathsy parts of engineering and I've heard it a lot.

About a decade ago I worked with a product manager who used that phrasing constantly, so it kind of stuck with me.

Surface it to say, that's my favorite lobe-earing eggcorn, for all intensive purposes!

It's a pretty common word if you've worked in anything that vaguely resembles an accountancy system. Also, anything crypto related will often use that word (the distributed ledger, etc)

Amen.

At work our documentation isn’t just getting littered with annoying jargon. It isn’t just all the hallucinations, either. (Since when has documentation ever been 100% trustworthy?) It’s that it’s so poorly written and structured that it’s becoming borderline incomprehensible.

My company is currently considering making, “Why should I bother to read something you didn’t bother to write?” an official policy because even the executives are starting to burn out on all the time they have to spend wading through slop.

Haven't done it, but letting an AI polish a manual first draft might be the best of both worlds?

This, a thousand times. As the ratio of code to human writing necessarily [1] goes up, they become not just smarter, but more precise and technical, which requires them to use more jargon. You could say they become more nerdy. Hence, text generated by these models will become more easily recognizable, at least by default, when not asking them to twist themselves into something else via prompting — which degrades intelligence. This is a good thing, in my book, given all the slop we already have to contend with.

Of course there will be models trained on much less code and technical writing, and they will create more natural sounding prose, but they will lack the deep intelligence of frontier models. Seems like a fair tradeoff.

[1] watch the first couple of minutes on this bycloud video on scaling training data mixtures: https://www.youtube.com/watch?v=aD93kfArOik

Agreed. The only goal of these skills/tricks/requests for humanising LLM writing is to be able to pass it off as your own, because they know it's shameful and want to avoid the opprobrium.

Some will say it's just for their own quality of life when they're reading LLM output, or "just for docs", but this is an extremely marginal use case.

> I want the ability to shame and discredit anyone passing the job of prose to a machine.

What about people who don’t speak your language well?

Because you can't actually do "good writing" by repeatedly applying the supposed idioms thereof. The tiny subsegment of humanity responsible for the RLHF don't necessarily have any good taste for writing; but even if they did, it's orders of magnitude harder to communicate than to make judgments of short samples, and communicating it by making those judgments is surely impossible.

Edit: I see that you got multiple replies all basically saying the same thing in very different words. There's an exquisite irony to that, I think.

It's not that the writing style is bad; in fact LLMs write actually pretty well. It's just too much overfitted. And even a style that, in itself, is pleasurable to read, becomes annoying when the same figures of speech are used over and over again.

Because LLMs are pattern-extenders that have nothing to say. The training overfitted to the grace notes in good writing. And since LLMs can’t wield language with purpose or experience the feeling of the words, they use these devices arbitrarily.

I think this is the same flaw as coding agents seeing in every problem the call for a “smoke test” or the use of some unnecessary design pattern. The truest part of AI is the A.

It's not that nobody likes it, in fact the problem is that people like each instance of it well enough in isolation. Millions of people think it's "good enough," so it gets amplified and repeated until every PR description starts to sound like a toothpaste jingle.

Because humans do like it, in reasonable quantities. The AI overlearns this and does it too much.

For "agentic use and coding," they are trained to take useful actions, not produce desirable natural language writing.

Maybe it’s the dead internet.

All the bots and other LLMs providing feedback, so in reality it’s reflecting the reality in a sense.

every one-hit wonder asks the same question.

we liked it until we didn't.

i hate it, but plenty of people DO like it and plenty of people talk and write like that. It’s just corpspeak, being used a lot in the valley and beyond. And all upcoming hustlers running startups now feel the need to speak like that, feeding this machine.

It might have been a prompt originally.

Examples? Do you use this pattern?

You could say it works perfectly well, it is identifying indeed. Of Claude and of people who use Claude's raw output instead of expressing themselves.

Wow sounds like you're streets ahead.

It's really frustrating, because now when I want to write something like a "not X but Y" or "you're absolutely right," I have to stop and decide if I want to self-censor to avoid sounding like a bot.

Sometimes those constructs are actually useful, but man has their overuse really killed them!

It was not intentional, and that's what makes this thing so weird. I wouldn't categorize my sentence that way because it's subtly different enough than the LLM version, which has a very punchy cadence.

Are you using the tools a lot and having first-hand exposure that gives you this sensitivity to phrasing? Or are you reacting to second-hand exposure? To a large degree, I've been isolating myself from the LLM craze. I have zero natural interest or impulse to prompt an LLM and read the results. Almost all my exposure is second-hand and involuntary. So, I haven't trained myself to know what phrasings are typical of which LLM product.

I don't feel as triggered LLM phrasing as people report here. At most, it feels like the same inane corporate jargon I've rolled my eyes at for my whole career. Perhaps it is amped up a bit, with too many forms of jargon multiplexed? It's a bit like when multilingual people code-switch too rapidly or even start to form some pidgin language. However, it is lacking the shared social context for this switching to be communicative. It's a bit more like spinning the dial on an old radio with random cuts between programming styles.

Stripped bare, I think What bugs me is the aggravated feeling that I am wading through word salad, and no longer being able to give the purveyor the benefit of the doubt. It was frustrating enough in the past, when it came from someone who was struggling to write or express themselves well. But now, it carries the implicit insult that they didn't even try, and it is constant and unrelenting.

So for me it's not the phrasing, it's that the phrases eventually don't add up. The meandering feels like a random walk. I get the same feeling from a lot of the egregious generated code I see in my day job. It's all superficial window dressing, but seems to miss the signature of an actual mind grappling with ideas and having intent to communicate.

It feels like we're trapped in some elaborate conceptual art piece, confronted by impenetrable symbolism. It invites nihilism but doesn't seem to actually reflect an artistic intent. The abyss gazes back...

If training models ever becomes 'cheap' for whatever definition of cheap you want to use, I suspect that will happen. With the current costs of a GDP of a small nation I don't see this likely for the time being.

It's like a new fad word. Gnarly, cool, bogus, rizz. When a few people use them it's new and interesting. When all of culture catches up and overuses them it's annoying as your gen-Z saying 6/7 40 times in a row.

The problem with millions of people using a few model is it's not 40 times in a row, it's 40 million!

I went through a “100%” phase recently and couldn’t for the life of me understand why I was suddenly saying it ALL THE TIME. Brains are so weird.

Introduce "hundo p" and "hundy" to her

Did you negotiate her down to "99%"?

I didn't articulate it, but what I meant was that I think we could swap these expressions out for _anything_, and we'd still find them irritating.

"Here's why this version is bulletproof" right before it fails in exactly the same way as the previous bulletproof implementation...

Who is we? Own your insults and the consequences of them sir.

When prompting an autoregressive token generator entity to do reasoning on a word logic puzzle you may find value in preferring it to produce rigorous predicate logic step notation with explicit delineation of its generated claims/hypotheses on where to look before wasting 30 dollars on a "debug this" prompt.

The industry will probably will probably coalesce around including the chat history in git MRs to reduce this shenanigans.

The humans are bristling because they wrote like that first. It's where Claude got it from!

If anything it proves the opposite imo.

That's a real distinction, you're right to point it out.

Nuance quietly surfaced.

Clean!

This one observation changes everything.

And it has teeth.

You are right ...

Well, "load-bearing" is specifically an engineering term :D Actual engineering, not software "engineering".

All of those phrases I've heard actively used even a decade (or two) ago. (I actually had to read your comment twice because I thought you were saying always, not never!)

"Critical path" and "long pole in tent" didn't make it into the model training data, but those were certainly also in play incessantly.

But they're all reasonably useful descriptions for common things, so I'm not surprised.

That's an importance nuance and makes the previous statements even more clear. I have the full picture now.

I heard "belt and suspenders" at least 20 years ago (meaning multiple solutions to a problem with backup in case one fails), and maybe would be longer if I were older. You could blame Claude for overusing it or importing it to other cultures maybe, but it's not in the category of invented phrases or ones that only barely mean something in the specific context Claude used them.

Personally my least favorite is the overuse of "quietly" (e.g. "No tricks. No marketing gimmicks. Just one company quietly outperforming the others"), and the one that makes the least sense to me is "that's the wedge."

I'm curious how these become so ingrained. Then the uncomfortable part is humans start repeating it more (a colleague said "belt-and-suspenders" during brainstorming the other day).

What's the difference between the two usages?

Take it to sonnet 5 or gpt and ask it to explain this to a layman. If you still don’t get it ask it for the why it matters or the how it relates.

You can also ask fable/4.8 to do it but I find it helps to keep the working model surrounded by the complexity rather than drawing it out. Simplifying text is something that takes relatively low effort in comparison to technical tasks. Sometimes I use Gemini, deepseek, grok, and recently meta just to see if they have any added perspectives, sometimes they do. Meta is really good at turning a technical mess into a story that paints a picture in my head.

I might need to do a spike as this is a core part of the spine. I will verify it and let you know when it's landed.

Yes, this and "belt-and-suspenders" are the ones that I notice the most. I also have non-native English speaking coworkers who have started using these terms/phrases recently, which makes me think that they're outsourcing all their writing.

Claude is obsessed with making things land. More than once I've reminded it that it's not a pilot.

I would add https://www.orwellfoundation.com/the-orwell-foundation/orwel...

("It consists in gumming together long strips of words which have already been set in order by someone else, and making the results presentable by sheer humbug" -- Orwell predicts the LLM)

and also https://www.jstor.org/stable/25515288 "The Myles na gCopaleen Catechism of Cliché" itself is rather hard to find online, but he's a very funny writer so it's worth the effort.

> Babel: https://en.wikipedia.org/wiki/Tower_of_Babel

I was hoping for a reference to the Babel Fish, whispering its translations in your ear.

It's not clear to me whether that tool exists or you hallucinated it into existence with this post

I'd love to see that tool.

You don't think me using "honest" there might have been a tiny bit of (on-topic, and therefore appropriate) trolling?

> but you don't see "load bearing" nearly as often in prose written by people

Unfortunately, we're starting to now.

Thanks to Claude.

You don't think it's possible that an LLM's internal machinery could decide that an underused-by-humans word should be used more frequently in output than it sees in input because it maps cleanly onto a frequently needed semantic? I think that's possible

And like any good corporate buzzword, it’s merely a simulacrum of precise technical jargon. The way Claude uses it is clearly wildly polysemous if not outright ambiguous.

What do you replace it with? "necessary dependency"?

> Exactly the same meaning and connotation.

No, it does not have the exact same meaning.

The first means that her optimism kept her in some functional state, without it, she would collapse.

The second means that her optimism continues over time, despite obstacles.

The first doesn't emphasise how longstanding her optimism is, the second does. The second doesn't emphasise how important her optimism is, the first does.

For me, "key", and "critical" merely say it's "important", but don't convey the sense that "out of the mess of connected concepts we're discussing, the one that is actually interacting with the thing we care about, or at least dominating the interactions with the thing we care about, is X".

"operative" is a bit better, but I think of it as referring to grammatical interactions, i.e. interactions at the level of language mechanics rather than semantics.

I actually think it's better. Back when access to knowledge was only available in English, there was a lot of mistranslated information in my language (Korean) that was worse than AI slop. These days, the translations are done by AI, so the tone may be awkward, but the content is more accurate than before, so I don't mind.

So that's the difference. I'm already living in a degraded environment, so this actually feels like an improvement to me. But you, coming from a better environment, perceive it as worse. It always seems to depend on cultural context.

The token saving is oversold, from what I can tell so far. These days output tokens are just the tip of the iceberg.

If anything the real value is it saves my brain from going into power saving mode by lunchtime because I haven’t spent the day reading pages of output when a sentence or two would do.

"Honest assessment: I was wrong to say I was being straight with you. You pointed out that a "smoking gun" is a sign of evidence, and I clearly didn't have any. This is not a bug but a gap that can be fixed like [this]. Give me the word and I'll wire it in."

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