So for example, current Claude models love "honest". They are always producing "honest" assessments. "The honest caveat" - I'm sorry, did you mean the caveat, period? But also, use the wrong phrasing and suddenly you can create your own word of the day for an AI model. I used the word "analytical" once, in a conversation with Gemini 3 Pro. I am pretty sure every single response from that point on had "analytical" in it at least once.
This is especially funny because system prompts and whatnot can also cause this behavior, but at least you can tweak those. You can't really do much about the model weights just having a weird affinity for a word.
I bet someone will or probably already has come up with a way to detect and prevent these problems during training or post training. I'm not saying it's an easy problem, but it has the benefit that it really should be detectable with just statistics.
> 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.
I'm sure the concept seemed just about purely preposterous to many when the models were in their infancy. Now I figure instead it seems mostly preposterous to many.
(Though I guess Anthropic‘s success doesn’t necessarily prove anything about the constitution)
But Sol actually has the same obsession with honesty: I suspect it's more an artifact of trying to control reward hacking.
Models will lie, obfuscate, and mislead under the pressure of RL, so both OAI and Ant are probably forced to spend a lot of time coaxing "honest" answers out of the model
OpenAI's recent prompt for a math conjecture hints at a lot of it when instructing on subagents: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98...
"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."
I grew up in the US South where starting or ending a sentence with "honest/honestly" was very common.
Because of behavioral / cultural norms, you might be very openly friendly with big smiles around a business customer that really grates on your nerves, or very openly nice to a neighbor that you really wish would move away and take their 3am welding and grinding in their garage with them.
Saying "honest/honestly" was seen as a "inside baseball" situation, where you were dropping social pretenses to tell someone your true opinion on a person or situation or whatever.
This also gets used inside companies between senior staff / management / directors / etc, as: "Okay, company politics and nonsense aside, I am being vulnerable here for a second and telling you what I really think about a $thing at potentially great job/advancement risk to myself".
Can it be meaningless? Yes.
Can the person say "honestly" and lie? Yes.
It has uses.
To this day, it's the only part I remember. I told them I would not promise, as everything I said was true. Making a specific promise would create an implication that I'm generally untruthful, unless I "promise".
I also could understand when a response hits someone like a ton of bricks, especially if their primal reaction is to go into denial mode. They might be looking for someone to kind of shake them and emphatically repeat the information they aren't thrilled about receiving. (or are thrilled about receiving! “Don’t get my hopes up, you’re serious right now?!“) And I imagine your response suited the purpose.
It’s classic you only remember the thought-provoking part. Reminded of “…people will remember how you made them feel…“
Sometimes people use it reflexively and doesn’t carry the same meaning (for me).
"Honestly, mom, I've never liked your fruitcake. I just ate it to make you happy."
"That's why you're my favorite child! Do you want another piece?"
"I'd love one."
Once the "honestly" is deployed, you have passed into my circle of trust, and are now privy to the pure, unvarnished version of events, not the glossy version management expects to be projected towards outsiders.
> Deliberately avoid a heavyweight "alert governance" process; the lightest recurring check that keeps FP-rate honest is the right dose.
And one for load bearing:
> Five open questions still stand; the load-bearing two are the runbook-AC contradiction (ratify "high-priority set only") and pinning the "high-priority set" definition + SLO source-of-truth before Milestone 3 (small-sample noise on a low-traffic fleet).
I want to say "ok, and now say that in a way that doesn't sound totally bizarre" yet instead I sigh and continue.
“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…
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.
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
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.
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...
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.
(This is intentional parody. Please don't shoot me.)
I am more pessimistic than that. Soon enough even people will start talking like LLMs. After listening to 5000 words per day, especially growing up, getting "help" with the homework, kids will start talking like LLMs.
- "Did you eat the cookies, Jimmy?"
- "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 we can go to the store and buy more".
FTFY
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.
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!"
EDIT: ok, here are two ways:
1. if it's merely a voice, I want to hear it. If it's slop, I want it taken out.
2. voice is signal, slop is noise; thus low-signal sentences are slop.
See, for example, "synergy", "proactive", "in the loop," and hundreds more that proliferate in corporate jargon with even more senselessness than the LLMs.
Real people think in concepts and experiences instead of words. The words are not so important to get the idea across, but LLMs only model language.
The problem is fundamental. There's no workaround. Averaging out word usage might even make the problem worse.
I learned about this opinion recently. It's interesting to me, because I very much think through words. I have an internal monologue that is running most of the time, and I often talk to myself, just start writing, or even record myself and transcribe to work through ideas, proposals, risks, etc. My understand is that some people don't have an internal monologue, and think purely in concept form. I was never like that.
"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.
We know exactly how they work. When we say they're impossible to analyze, i.e. for particular traits like this, it means that the data model is so big that tracing it would be logistically impossible because of the scale involved and time constraints.
For comparison, suppose you tried to analyze all the nooks and crannies of the Amazon watershed to find out why a particular rock appears at the delta. You could follow it back to the exact tributary, but it'll take forever, and is it worth the effort when you're going to start from scratch with the next rock?
The brain too sits locked inside a bone box and only gets a bundle of unlabeled nerves connecting it to the outside. How can the brain could possibly experience anything, it only sees patters and patterns of patterns never the real thing?
As a species, we do need to up our cable management skills. We're likely not getting augmented humans until we get there.
If I use the word "semantic", do you have a concept of what it means?
If so, can you please share which of your senses have shaped the world experience that inform this concept? What have you smelled, tasted, caressed, that informed this concept outside of words?
If I make up the word "polysemantic", do you need to recall a personal experience of polyamory to understand it, or could you possibly use your concept of "poly" and your concept of "semantic" to figure out this new concept?
Does the material universe perform any other acts than organizing information?
I feel like you're trying to make me argue a position I'm not defending here.
The research goals were and still are clearly distinct from the business goals.
This isn't people merely annoyed with repetition. This is the majority of people realizing the limitations of LLMs. Why would researchers give a flying crap about the ignorance of the business world and the public?
/s