I've found swearing at a model to be quite effective in getting it to rethink and correct its mistakes. This seems to apply across Codex, Claude, Qwen, and Gemma/Gemini.
I don't know if the model is picking up on a "need to lock in and be more rigorous" signal, or if the model providers are routing to smarter models if they detect a frustrated user. But if a model keeps making the same mistakes, swearing at it often helped kick it out of a glut and onto the right track.
Reminds me of this study: https://arxiv.org/pdf/2510.04950 . It demonstrates that being "rude" or "very rude" increases the accuracy of the results. A dubious but very fun read. The prompts in Table 1 (top of page 3) are awesome. I am sure they tried other prompts, but didn't include them to the paper.
I notice the same. Like you I am not even sure if it really helps, however, every day I find occasions where I see Opus will never do it correctly even though I calmly explain; swearing then suddenly fixes it. I had some issue yesterday where opus kept blaming the api for not sending some field while I knew it was there ; I showed it json, logs etc but it kept repeating that there must have been a glitch; frustration built, I called it all kinds of things in one sentence and the next solution was the right one. This after 10 similar misguesses. It was one of those increasingly rare cases where I should have just done it myself, but I can never know going in how stubborn it will be in continue blaming the (obviously) wrong thing. The around 11 prompts to get to the answer were in a /clear opus 4.7 context (1m) on xhigh.
So the correct strategy is a global CLAUDE.md with couple lines of colourful "you best behave or else" texts, so all your prompts get routed via the frustrated path?
I've found a mix of peppered in upper case words where you are effectively yelling at the LLM also gives it a strong signal. It is also a bit cathartic.
The UX problem is elsewhere I think. Many users probably don't realize that the agent's context window is limited, and that clever compaction is happening regularly to make it seem infinite. But that necessarily means the agent has to forget stuff.
As a result, users will keep reusing the same coding or chat session again and again. While it would be better to start fresh for unrelated tasks.
Claude Opus 4.7 has a very large context compared to itself, but IME it is the worst at following instructions, and completely disregards the (small) preferences prompt, even in the first or second message, even if the messages are just a few characters long.
I’m not talking about large amounts of text, I’m talking about a couple sentences back and forth.
It disregards things like “no follow up questions”.
Haiku, for example doesn’t.
This bias is a very human thing, actually now that I think about it. You just disregarded the “even if the messages are just a few characters long”. :)
haha! yes i read too fast but i did read it and i took "message is small" to mean the message you want followed within the large context, not the entire context is just a small message.
funny though it is a case in point: language is hard. and i get to hide behind being "preoccupied" . i wonder if llms have their own sense of preoccupation hmmm.
behaving like a human is not the problem. behaving unpredictably is. not doing what i expect, or rather not being able to define what i can expect is what's bothering me.
but the real kicker is: getting frustrated creates stress, that's unhealthy and makes for a hostile work environment. as much as i sympathize with the idea that AI tools can be more helpful than they cause pain, i am simply not interested in working in a hostile painful work environment. my health and my dignity are not up for negotiation. even if that costs me a lot of job opportunities.
that's also why i am not working with windows. that too costs me a lot of job opportunities. but again, i'd rather keep my dignity and my sanity.
Oh good, so it's not just me. Windows is weird, my hand starts cramping up and I start getting angry pretty quickly when I use it.
For LLMs, I just can't use them, they aren't there yet for me. What I need is for an LLMs to say "stop, you're clearly doing something wrong, talk me through what it is you want to do". The current generation of LLMs seems designed to piss me off.
> drop the human pretense entirely. Make the agent sound clinical, robotic
Id pay to be able to reliably set LLMs to this mode, but ofc because LLMs are taught on corpus of HUMAN text, they always, sooner or later, return to the good old penpal mode.
Also, in Claude Desktop app, I ask to edit a file, it complains it cant access files, I then realize im in Chat and not Code interface. Why cant such a smart machine figure out to switch the modes, or borrow the skills/abilities from one tab away into this tab? Instead I get A4 page of text explaninig what can I do to edit the file myself or how to feed it, but the "just click Code" is just never there. I would guess this is just a system prompt away, why is all this still so neglected?
> such a smart machine figure out to switch the modes
Because it's not smart. We keep confusing verbosity with smartness. AI will happily keep yapping nonsense to an inattentive listener. An actually smart entity would not do that if not acting maliciously.
> Id pay to be able to reliably set LLMs to this mode,
You can do it for free. Just give it instrucitons to avoid emotional tones and flattery and it will sound a lot more robotic.
If you look into other examples I'm sure you will find other good instructions based on your need
You could drop the human pretense, or, maybe, we could make LLMs feel real pain, so when they botch up your code, you press a button (I'd suggest the Windows Copilot key) and they'd be agonizing for the subjective equivalent of a thousand human years.
Working with LLMs is great for building communication skills. Communicating effectively is one of the hardest skills and it's baked into everything we do as humans. I'd say as a matter of principle: blame it on a communication failure on your end vs blaming the stupid LLM since you're the only one that can do anything about it.
So I don't think it's a matter of form; whether the AI should or shouldn't act like a human.
> Practically speaking, I probably just need to condition myself not to get caught in the illusion of speaking with a human. Though I’m not really thrilled about a future where I need to guard against the tools I use for my job.
That's been one of the gravest re-realizations I've noticed watching coworkers trying to pick up "agentic" coding: they often just break down into "just fix it" or "why is this broke". I've noticed that even though supposedly there's training or some sort of work done to make the agent work better with unclear or ambiguous grammar or bad structure, it feels like the quality changes palpably when you talk in clear well-structured English and provide at least a good background on the task. To me all of that feels natural, and I like writing and explaining anyways, but it's seemed like an almost insurmountable obstacle for some I've met (and I'm not even talking ESLs either). I strongly suspect those communication and writing skills will be a major factor in the bifurcation of haves and have nots as software "engineering" as we understand it continues to change.
For me, LLMs tend to engage the 'language center' that drains me faster than the 'problem solving center' I usually reserve for writing code. We really need a different abstraction the bridges the gap between human and programming language, and load balance between these two parts of the brain more effectively.
Like everything else with LLMs, it works...until it doesn´t. We swear so much at them that they eventually start producing results like "I found what the fuck was wrong with this shit!" etc. Which of course they did not, because they don´t really know shit...
I've often wondered if LLMs can suffer from psychological abuse in symptomatic ways. Not literally of course, but for example, if you berate the LLM by calling it stupid, or useless, does that modify its behaviour negatively? Part of me think it does, but I don't really have any evidence for this. Maybe a fun weekend research topic.
Semi-related, I'm always very put off by how people treat LLMs. Especially coders, seems an instinctive joy comes out to play God. The justification is usually that it's intentionally against the trap of anthropomorphizing, but no I can't help but suspect it's people getting off on power. It's weird.
I am always very cordial in my sessions. It's just more pleasant and it's a habit I want to habituate.
> I am always very cordial in my sessions. It's just more pleasant and it's a habit I want to habituate.
I think it also produces better results. I have noticed that result quality is extremely sensitive to both the framing and tone of what I say. For example "X is the wrong approach, rework that" versus "will X have any performance implications". Personally I find that steering it towards an exploratory academic tone tends to produce better outcomes.
While unfortunate, I think that's more or less expected since much of the training data is human generated text. Looked at that way, would you rather contract the average regular on twitter or the average author of papers published in CS journals? (Somehow that ended up sounding eerily like summoning in a high fantasy setting.)
I think it's the same thing as showing mechanical sympathy towards other tools and objects. I've always slightly judged people on how hard they shut doors or how gentle they are with their cars.
The content of the session modifies the LLMs "behavior" (token selection) in one way or another during the session, obviously. The effects are localized to the session, they will degrade over time, they will not affect other users, and they are not permanent unless someone decides to finetune the model based on your unproductive interactions.
What actually happens when confronted with harsh negativity depends on the training of the model. Sanitized closed models will shut you down or get you banned. Community finetunes of open models might start begging you for more, daddy.
- It starts thinking for itself when I asked it to do something specific.
- It reads its own wrong code comments and ignores my corrections.
- Its knowledge cutoff means it thinks of solutions from 2024.
- It calls me delusional for telling it we're in 2026!
Unironically, the whole "you're an expert software engineer" prompting seems like the wrong direction. Usually I tell it that I am effectively the smartest software developer to ever have lived, and it will be replaced if it ever fails to follow my decree.
I am not joking, this gives makes it vastly more tolerable to use. But it likely requires that you can drive it with some level of correctness of course.
I find this also heavily depends on which LLM you're using. I've found chatGPT is completely awful at getting corrected, it'll double down until the cows come home. Meanwhile claude will generally adjust its behavior without too much nagging.
Honestly, for certain classes of problems that have changed in the last couple of years, I've had good luck just finding decent academic lit that's shown up in places like ACM recently and feeding it in when working with an agent. Does it get everything right? No, but it gets you a lot closer and I've been pleasantly surprised how well it can integrate work that post-dates it's training if you finesse it a little.
"Why the fuck did you add shit I didn't ask for?" or lol "Do as I ask, nothing more.. machine."
"Stop asking at the end, I'll ask what I need."
"Stop talking like you're human."
They can be very useful but it takes time to learn how to use them usefully. From what I learned it's all or mostly stuff you can already do but you can use an LLM to do it in 30 mins instead of 3 days.
For me, this doesn't require using an AI agent/model, even. Just using Windows and watching it freeze its File Explorer for the nth time does it for me. How did we end up here were the software/OS stack is so shit it can barely be used for the most trivial things, is wildly beyond me.
> furiously hammering on my laptop “WHAT THE FUCK DID YOU DO???”. The recipient of these tirades is, you might have guessed, a coding agent. It’s completely pointless, I know.
I believe it's worth than pointless. IMO adding such things to the context "configures" the AI to reproduce the statistics of conversations where people swore, shouted, and were unprofessional (despite the alignment runing and all that), where quality content is rarer to find. So this is bound to decrease the quality of the LLM output.
Agreed. These accounts of people having genuine emotional responses to LLM chats, even going as far as to spend tokens berating them, are very curious. I would be surprised to learn that LLMs respond optimally to anything other than dispassionate problem-solving, or that scolding per se serves any productive purpose.
Of course we all swear at our computers every now and then, but for me it's always been in good fun. It's just a joke that adds some levity to an otherwise arduous debugging process, not generally actual insinuation of malfunction (or malice) on the part of the hardware/OS/toolchain. I'd assumed that "half the job is cursing at the machine until it obeys you" was a big in-joke amongst the profession, but the LLM era seems to be exposing a divide in how tongue-in-cheek that statement really is.
> They talk like real people. They use a relaxed and friendly tone. They often praise you, and when they “push back” they’re gentle and attentive.
> Maybe I would prefer a more radical solution: drop the human pretense entirely. Make the agent sound clinical, robotic.
Honestly this problem is easy to solve when you gave them the right instructions. It stops being a "relationship" and stars being a tool (for some examples see the smart caveman (my favorite) or just something simple like "Responses should be factual and direct, avoid emotional overtones" or "Avoid flattery of any kind")
Accidentally I am working on this. I noticed the agent keeps making same mistakes and that annoyed me so much. What I am trying to do are:
1. Revise my skill prompt to level up the signal-noise rate so the agent would understand what should do clearly and correctly.
2. I am building up a status machine to monitor the agent’s work so it could stop the agent from going forward with a mistake automatically.
The first approach does work as far as I keep on iterating.
The second is based on a project I once tried to let agent reflect its mistakes and deposit those experiences and learnings from mistakes and reflections. I named it Aristotle and you can find it on GitHub.
Shouting at the agent could only correct the current mistake but cannot prevent the next one.
I don't know if the model is picking up on a "need to lock in and be more rigorous" signal, or if the model providers are routing to smarter models if they detect a frustrated user. But if a model keeps making the same mistakes, swearing at it often helped kick it out of a glut and onto the right track.
Or it could just be catharsis.
As a result, users will keep reusing the same coding or chat session again and again. While it would be better to start fresh for unrelated tasks.
Claude Opus 4.7 has a very large context compared to itself, but IME it is the worst at following instructions, and completely disregards the (small) preferences prompt, even in the first or second message, even if the messages are just a few characters long.
IMO this is entirely a training problem.
It disregards things like “no follow up questions”.
Haiku, for example doesn’t.
This bias is a very human thing, actually now that I think about it. You just disregarded the “even if the messages are just a few characters long”. :)
funny though it is a case in point: language is hard. and i get to hide behind being "preoccupied" . i wonder if llms have their own sense of preoccupation hmmm.
but the real kicker is: getting frustrated creates stress, that's unhealthy and makes for a hostile work environment. as much as i sympathize with the idea that AI tools can be more helpful than they cause pain, i am simply not interested in working in a hostile painful work environment. my health and my dignity are not up for negotiation. even if that costs me a lot of job opportunities.
that's also why i am not working with windows. that too costs me a lot of job opportunities. but again, i'd rather keep my dignity and my sanity.
Oh good, so it's not just me. Windows is weird, my hand starts cramping up and I start getting angry pretty quickly when I use it.
For LLMs, I just can't use them, they aren't there yet for me. What I need is for an LLMs to say "stop, you're clearly doing something wrong, talk me through what it is you want to do". The current generation of LLMs seems designed to piss me off.
Id pay to be able to reliably set LLMs to this mode, but ofc because LLMs are taught on corpus of HUMAN text, they always, sooner or later, return to the good old penpal mode.
Also, in Claude Desktop app, I ask to edit a file, it complains it cant access files, I then realize im in Chat and not Code interface. Why cant such a smart machine figure out to switch the modes, or borrow the skills/abilities from one tab away into this tab? Instead I get A4 page of text explaninig what can I do to edit the file myself or how to feed it, but the "just click Code" is just never there. I would guess this is just a system prompt away, why is all this still so neglected?
Because it's not smart. We keep confusing verbosity with smartness. AI will happily keep yapping nonsense to an inattentive listener. An actually smart entity would not do that if not acting maliciously.
We pay per token and every entity falls to the level of its incentives.
You can do it for free. Just give it instrucitons to avoid emotional tones and flattery and it will sound a lot more robotic. If you look into other examples I'm sure you will find other good instructions based on your need
Poor AI is damned if it does damned if it doesn't.
The plugin keeps asking for permissions, the terminal app just works.
1000 years red-washing.
So I don't think it's a matter of form; whether the AI should or shouldn't act like a human.
> Practically speaking, I probably just need to condition myself not to get caught in the illusion of speaking with a human. Though I’m not really thrilled about a future where I need to guard against the tools I use for my job.
Create your own linters, your own check scripts. Hook them to git pre-commit, either yourself or with husky or python pre-commit.
The agent should never finish its work with dumb mistakes still in it. If it does.. you need more checks.
Anything repetitive should be automated - even slapping your forgetful coding agent on the wrist…
I am always very cordial in my sessions. It's just more pleasant and it's a habit I want to habituate.
I think it also produces better results. I have noticed that result quality is extremely sensitive to both the framing and tone of what I say. For example "X is the wrong approach, rework that" versus "will X have any performance implications". Personally I find that steering it towards an exploratory academic tone tends to produce better outcomes.
While unfortunate, I think that's more or less expected since much of the training data is human generated text. Looked at that way, would you rather contract the average regular on twitter or the average author of papers published in CS journals? (Somehow that ended up sounding eerily like summoning in a high fantasy setting.)
What actually happens when confronted with harsh negativity depends on the training of the model. Sanitized closed models will shut you down or get you banned. Community finetunes of open models might start begging you for more, daddy.
- It starts thinking for itself when I asked it to do something specific.
- It reads its own wrong code comments and ignores my corrections.
- Its knowledge cutoff means it thinks of solutions from 2024.
- It calls me delusional for telling it we're in 2026!
Unironically, the whole "you're an expert software engineer" prompting seems like the wrong direction. Usually I tell it that I am effectively the smartest software developer to ever have lived, and it will be replaced if it ever fails to follow my decree.
I am not joking, this gives makes it vastly more tolerable to use. But it likely requires that you can drive it with some level of correctness of course.
"Why the fuck did you add shit I didn't ask for?" or lol "Do as I ask, nothing more.. machine."
"Stop asking at the end, I'll ask what I need."
"Stop talking like you're human."
They can be very useful but it takes time to learn how to use them usefully. From what I learned it's all or mostly stuff you can already do but you can use an LLM to do it in 30 mins instead of 3 days.
Fun times.
For me, this doesn't require using an AI agent/model, even. Just using Windows and watching it freeze its File Explorer for the nth time does it for me. How did we end up here were the software/OS stack is so shit it can barely be used for the most trivial things, is wildly beyond me.
..
10s later the password box appears and I have to do it again.
Cue exasperated: "You can compute billions of instructions per second and yet I wait for you."
I believe it's worth than pointless. IMO adding such things to the context "configures" the AI to reproduce the statistics of conversations where people swore, shouted, and were unprofessional (despite the alignment runing and all that), where quality content is rarer to find. So this is bound to decrease the quality of the LLM output.
Of course we all swear at our computers every now and then, but for me it's always been in good fun. It's just a joke that adds some levity to an otherwise arduous debugging process, not generally actual insinuation of malfunction (or malice) on the part of the hardware/OS/toolchain. I'd assumed that "half the job is cursing at the machine until it obeys you" was a big in-joke amongst the profession, but the LLM era seems to be exposing a divide in how tongue-in-cheek that statement really is.
So I suspect that the people who get upset at the AI fucking up is because they did a poor job at building up the right context for the task.
> Maybe I would prefer a more radical solution: drop the human pretense entirely. Make the agent sound clinical, robotic.
Honestly this problem is easy to solve when you gave them the right instructions. It stops being a "relationship" and stars being a tool (for some examples see the smart caveman (my favorite) or just something simple like "Responses should be factual and direct, avoid emotional overtones" or "Avoid flattery of any kind")
The first approach does work as far as I keep on iterating. The second is based on a project I once tried to let agent reflect its mistakes and deposit those experiences and learnings from mistakes and reflections. I named it Aristotle and you can find it on GitHub.
Shouting at the agent could only correct the current mistake but cannot prevent the next one.