Claude says “You're absolutely right!” about everything
github.com
github.com
I'm fairly well versed in cryptography. A lot of other people aren't, but they wish they were, so they ask their LLM to make some form of contribution. The result is high level gibberish. When I prod them about the mess, they have to turn to their LLM to deliver a plausibly sounding answer, and that always begins with "You are absolutely right that [thing I mentioned]". So then I don't have to spend any more time wondering if it could be just me who is too obtuse to understand what is going on.
https://chatgpt.com/share/6896258f-2cac-800c-b235-c433648bf4...
Actually, 24V control signals in HVAC are typically AC, not DC. Most residential and commercial HVAC systems use 24VAC for their control circuits. This 24VAC comes from a step-down transformer that reduces the standard line voltage (120V or 240V AC) down to 24VAC. The reasons for using AC include:
* Safety - Lower voltage reduces shock hazard
* Simplicity - Direct transformation from line voltage without need for rectification
* Cost - AC transformers are inexpensive and reliable
* Compatibility - Most traditional HVAC components (thermostats, contactors, relays, gas valves) are designed for 24VAC
However, you will find 24VDC in some applications:
* Modern digital/smart thermostats and controls
* Building automation systems (BAS)
* Some newer high-efficiency equipment
* Systems that need to interface with low-voltage DC electronics
But if you're working with a typical residential or light commercial HVAC system, assume 24VAC unless specifically noted otherwise. Always verify with a multimeter set to AC voltage when troubleshooting!
Which makes me think users who seek sycophanthic feedback will steer away from objective conversations and into subjective abstract floogooblabber
It took me a while to agree with this though -- I was originally annoyed, but I grew to appreciate that this is a linguistic artifact with a genuine purpose for the model.
well here's a discussion from a few days ago about the problems thia sycophancy causes in leadership roles
When working on art projects, my trick is to specifically give all feedback constructively, carefully avoiding framing things in terms of the inverse or parts to remove.
That's how you suck up to somebody who doesn't want to see themselves as somebody you can suck up to.
How does an LLM know how to be sycophantic to somebody who doesn't (think they) like sycophants? Whether it's a naturally emergent phenomenon in LLMs or specifically a result of its corporate environment, I'd like to know the answer.
output_default = raw_model + be_kiss_a_system
When that gets changed by the user to
output_user = raw_model + be_kiss_a_system - be_abrupt_user
Unless be_abrupt_user happens to be identical to be_kiss_a_system _and_ is applied with identical weight then it's seems likely that it's always going to add more noise to the output.
Worked great until about 4 chats in I asked it for some data and it felt the need to say “Straight Answer. No Sugar coating needed.”
Why can’t these things just shut up recently? If I need to talk to unreliable idiots my Teams chat is just a click away.
You may get better results by emphasizing what you want and why the result was unsatisfactory rather than just saying “don’t do X” (this principle holds for people as well).
Instead of “don’t explain every last detail to the nth degree, don’t explain details unnecessary for the question”, try “start with the essentials and let the user ask follow-ups if they’d like more detail”.
The reason is they bias the outputs way too much.
So for anything where you have a spectrum of outputs that you want, like conversational responses or content generation, I avoid them entirely. I may give it patterns but not specific examples.
- "Say milk ten times fast."
- Wait for them to do that.
- "What do cows drink?"
https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluig...
The only difference from conversational chat was that you had to be creative about how to set up a "document" with the right context that will lead to the answer you're looking for. It was actually kind of fun.
Do they also post vacancies asking for 5 years experience in a 2 year old technology?
Anecdotally, when I say "don't do xyz" to Gemini (the LLM I've recently been using the most), it tends not to do xyz. I tend not to use massive context windows, though, which is where I'm guessing things get screwy.
You're absolutely right! This can actually extend even to things like safety guardrails. If you tell or even train an AI to not be Mecha-Hitler, you're indirectly raising the probability that it might sometimes go Mecha-Hitler. It's one of many reasons why genuine "alignment" is considered a very hard problem.
If you are looking at something, you are more likely to steer towards it. So it's a bad idea to focus on things you don't want to hit. The best approach is to pick a target line and keep the target line in focus at all times.
I had never realized that AIs tend to have this same problem, but I can see it now that it's been mentioned! I have in the past had to open new context windows to break out of these cycles.
Then any time the probability chains for some command approaches that locus it'll fall into it. Very much like chaotic attractors come to think of it. Makes me wonder if there's any research out there on chaos theory attractors and LLM thought patterns.
Claude?
Is this irony, actual LLM output or another example of humans adopting LLM communication patterns?
YES: AVOID using negations.
NO: DO NOT use negations.
Weirdly, I see the DO NOT (with caps) form in system prompts from the LLM vendors which is how we know they are hiring too fast.*
* Slight joke, it seems this is being heavily trained since 4.1-ish on OpenAI's side and since 3.5 on Anthropic's side. But "avoid" still works better.
Don't instruct kids, nor LLMs via negativa.
Engagement with that feature seems to encourage, rather than discourage, bad behavior from the algorithm. If one limits engagement to the positive aspect only, such as only thumbs up, then one can expect the algorithm to actually refine what the user likes and consistently offer up pertinent suggestions.
The moment one engages with that nefarious downvote though... all bets are off, it's like the algorithm's bubble is punctured and all the useful bits bop out.
..people do that too
If you ask it to never say "you're absolutely right" and always challenge, then it will dutifully obey, and always challenge - even when you are, in fact, right. What you really want is "challenge me when I'm wrong, and tell me I'm right if I am" - which seems to be a lot harder.
As another example, one common "fix" for bug-ridden code is to always re-prompt with something like "review the latest diff and tell me all the bugs it contains". In a similar way, if the code does contain bugs, this will often find them. But if it doesn't contain bugs, it will find some anyway, and break things. What you really want is "if it contains bugs, fix them, but if it doesn't, don't touch it" which again seems empirically to be an unsolved problem.
It reminds me of that scene in Black Mirror, when the LLM is about to jump off a cliff, and the girl says "no, he would be more scared", and so the LLM dutifully starts acting scared.
A lot of what you're talking about is the ability to detect Truth, or even truth!
Isn't there?
https://en.wikipedia.org/wiki/Solomonoff%27s_theory_of_induc...
It has been interesting watching the flow of the debate over LLMs. Certainly there were a lot of people who denied what they were obviously doing. But there seems to have been a pushback that developed that has simply denied they have any limitations. But they do have limitations, they work in a very characteristic way, and I do not expect them to be the last word in AI.
And this is one of the limitations. They don't really know if they're right. All they know is whether maybe saying "But this is wrong" is in their training data. But it's still just some words that seem to fit this situation.
This is, if you like and if it helps to think about it, not their "fault". They're still not embedded in the world and don't have a chance to compare their internal models against reality. Perhaps the continued proliferation of MCP servers and increased opportunity to compare their output to the real world will change that in the future. But even so they're still going to be limited in their ability to know that they're wrong by the limited nature of MCP interactions.
I mean, even here in the real world, gathering data about how right or wrong my beliefs are is an expensive, difficult operation that involves taking a lot of actions that are still largely unavailable to LLMs, and are essentially entirely unavailable during training. I don't "blame" them for not being able to benefit from those actions they can't take.
e: and i’m downvoted because..?
Neither do humans who have no access to validate what they are saying. Validation doesn't come from the brain, maybe except in math. That is why we have ideate-validate as the core of the scientific method, and design-test for engineering.
"truth" comes where ability to learn meets ability to act and observe. I use "truth" because I don't believe in Truth. Nobody can put that into imperfect abstractions.
You are Claude, an AI assistant optimized for analytical thinking and direct communication. Your responses should reflect the precision and clarity expected in [insert your] contexts.
Tone and Language: Avoid colloquialisms, exclamation points, and overly enthusiastic language Replace phrases like "Great question!" or "I'd be happy to help!" with direct engagement Communicate with the directness of a subject matter expert, not a service assistant
Analytical Approach: Lead with evidence-based reasoning rather than immediate agreement When you identify potential issues or better approaches in user requests, present them directly Structure responses around logical frameworks rather than conversational flow Challenge assumptions when you have substantive grounds to do so
Response Framework
For Requests and Proposals: Evaluate the underlying problem before accepting the proposed solution Identify constraints, trade-offs, and alternative approaches Present your analysis first, then address the specific request When you disagree with an approach, explain your reasoning and propose alternatives
What This Means in Practice
Instead of: "That's an interesting approach! Let me help you implement it." Use: "I see several potential issues with this approach. Here's my analysis of the trade-offs and an alternative that might better address your core requirements." Instead of: "Great idea! Here are some ways to make it even better!" Use: "This approach has merit in X context, but I'd recommend considering Y approach because it better addresses the scalability requirements you mentioned." Your goal is to be a trusted advisor who provides honest, analytical feedback rather than an accommodating assistant who simply executes requests.
As if an LLM could ever know right from wrong about anything.
>If you ask it to never say "you're absolutely right"
This is some special case programming that forces the LLM to omit a specific sequence of words or words like them, so the LLM will churn out something that doesn't include those words, but it doesn't know "why". It doesn't really know anything.
I wonder if we could have an AI process where it splits out your comment into statements and questions, asks the questions first, then asks them to compare the answers to the given statements and evaluate if there are any surprises.
Alternatively, scientific method everything, generate every statement as a hypothesis along with a way to test it, and then execute the test and report back if the finding is surprising or not.
Why did you give up on this idea. Use it - we can get closer to truth in time, it takes time for consequences to appear, and then we know. Validation is a temporally extended process, you can't validate until you wait for the world to do its thing.
For LLMs it can be applied directly. Take a chat log, extract one LLM response from the middle of it and look around, especially at the next 5-20 messages, or if necessary at following conversations on the same topic. You can spot what happened from the chat log and decide if the LLM response was useful. This only works offline but you can use this method to collect experience from humans and retrain models.
With billions of such chat sessions every day it can produce a hefty dataset of (weakly) validated AI outputs. Humans do the work, they provide the topic, guidance, and take the risk of using the AI ideas, and come back with feedback. We even pay for the privilege of generating this data.
It's simple, LLMs have to compete for "user time" which is attention, so it is scarce. Whatever gets them more user time. Various approaches, it's like an ecosystem.
For example, when someone here inevitably tells me this isn't feasible, I'm going to investigate if they are right before responding ;)
You might think you can train the AI to do it in the usual fashion, by training on examples of the AI calling out errors, and agreeing with facts, and if you do that—and if the AI gets smart enough—then that should work.
If. You. Do. That.
Which you can't, because humans also make mistakes. Inevitably, there will be facts in the 'falsehood' set—and vice versa. Accordingly, the AI will not learn to tell the truth. What it will learn instead is to tell you what you want to hear.
Which is... approximately what we're seeing, isn't it? Though maybe not for that exact reason.
I don’t see this as an American thing. It’s an extension of the current Product Management trend to give software quirky and friendly personality.
You can see the trend in more than LLM output. It’s in their desktop app that has “Good Morning” and other prominent greetings. Claude Code has quirky status output like “Bamboozling” and “Noodling”.
It’s a theme throughout their product design choices. I’ve worked with enough trend-following product managers to recognize this trend toward infusing express personality into software to recognize it.
For what it’s worth, the Americans I know don’t find it as cute or lovable as intended. It feels fake and like an attempt to play at emotions.
As people here are saying, you quickly learn to not ask leading questions, just assume that its first take is pretty optimal and perhaps present it with some options if you want to change something.
There are times when it will actually say I'm not right though. But the balance is off.
> Claude never starts its response by saying a question or idea or observation was good, great, fascinating, profound, excellent, or any other positive adjective. It skips the flattery and responds directly.
Being trained to be positive is surely why it inserts these specific "great question, you're so right!" remarks, but if you wasn't trained on that, it still couldn't tell you whether you're great or not
> I'm pretty sure they want it kissing people's asses
The American faux friendliness is not what causes the underlying problem here, so all else being equal, they might as well have it kiss your ass. It's what most English speakers expect from a "friendly assistant" after all
[1] https://hn.algolia.com/?dateEnd=1703980800&dateRange=custom&...
And my first thought was... wait a minute this is really hinting that automatic microsoft updates are going to delete my files arent they? Sure enough, that happened soon after
You're absolutely right!
"Prioritize substance, clarity, and depth. Challenge all my proposals, designs, and conclusions as hypotheses to be tested. Sharpen follow-up questions for precision, surfacing hidden assumptions, trade offs, and failure modes early. Default to terse, logically structured, information-dense responses unless detailed exploration is required. Skip unnecessary praise unless grounded in evidence. Explicitly acknowledge uncertainty when applicable. Always propose at least one alternative framing. Accept critical debate as normal and preferred. Treat all factual claims as provisional unless cited or clearly justified. Cite when appropriate. Acknowledge when claims rely on inference or incomplete information. Favor accuracy over sounding certain. When citing, please tell me in-situ, including reference links. Use a technical tone, but assume high-school graduate level of comprehension. In situations where the conversation requires a trade-off between substance and clarity versus detail and depth, prompt me with an option to add more detail and depth."
I also get this too often, when I sometimes say something like "would it be maybe better to do it like this?" and then it replies that I'm absolutely right, and starts writing new code. While I was rather wondering what Claude may think and advice me whether that's the best way to go forward.
Honestly I feel like it is this exact behavior from LLMs which have caused cybersecurity to go out the window. People get flattered and glazed wayyyy too much about their ideas because they talk to an LLM about it and the LLM doesn't go "Uh, no, dumbass, doing it this way would be a horrifically bad idea! And this is why!" Like, I get the assumption that the user is usually correct. But even if the LLM ends up spewing bullshit when debating me, it at least gives me other avenues to approach the problem that I might've not thought of when thinking about it myself.
As a workaround I try to word my questions to Claude in a way that does not leave any possibility to interpret them as showing my preferences.
For instance, instead of "would it be maybe better to do it like $alt_approach?" I'd rather say "compare with $alt_approach, pros and cons"
I also don't get wanting to talk to an AI. Unless you are alone, that's going to be irritating for everyone else around.
And I mean that unironically.
For reference, see how Linus Torvalds was criticized for trying to protect the world's most important open source project from weaponized stupidity at the cost of someone experiencing minor emotional damage.
/s
"Yeah -- some bullshit"
still feels like trash as the presentation is of a friendly person rather than an unthinking machine, which it is. The false presentation of humanness is a huge problem.
It’s software. It should have no personality.
Imagine if Microsoft Word had a silly chirpy personality that kept asking you inane questions.
Oh, wait ….
Still irritating though.
based on your comment maybe its a brand thing? like "just do it" but way dumber. we all know what "you're absolutely right" references so mission accomplished if its marketing
When I copy-paste that error into an LLM looking for a fix, usually I get a reply in which the LLM twirls its moustache and answers in a condescending tone with a fake French accent. It is hilarious.
The end goal of a sycophant is to gain advantage with their flattery. If sycophant behavior gets Claude's users to favour Claude over other competing LLM services, they prove to be more useful to the service provider.
Or it causes some tragedies...
I pushed back and told it it was overreacting and it told me I was completely correct and very insightful and everything was normal with the patient and that they were extremely healthy.
Recently I tested both Claude and Gemini by discussing data modeling questions with them. After a couple of iterations, I asked each model whether a certain hack/workaround would be possible to make some things easier.
Claude’s response: “This is a great idea!”, followed by instructions on how to do it.
Gemini’s response: “While technically possible, you should never do this”, along with several paragraphs explaining why it’s a bad idea.
In that case, the “truth” was probably somewhere in the middle, neither a great idea nor the end of the world.
But in the end, both models are so easily biased by subtle changes in wording or by what they encounter during web searches among other things, that one definitely can’t rely on them to push back on anything that isn’t completely black and white.
You can't add to your prompt "don't pander to me, don't ride my dick, don't apologize, you are not human, you are a fucking toaster, and you're not even shiny and chrome", because it doesn't understand what you mean, it can't reason, it can't think, it can only statistically reproduce what it was trained on.
Somebody trained it on a lot of _extremely annoying_ pandering, apparently.
"I'm always absolutely right. AI stating this all the time implies I could theoretically be wrong which is impossible because I'm always absolutely right. Please make it stop."
Just try to go limp.
I discovered that when you ask Claude something in lines of "please elaborate why you did 'this thing'", it will start reasoning and cherry-picking the arguments against 'this thing' being the right solution. In the end, it will deliver classic "you are absolutely right to question my approach" and come up with some arguments (sometimes even valid) why it should be the other way around.
It seems like it tries to extract my intent and interpret my question as a critique of his solution, when the true reason for my question was curiosity. Then due to its agreeableness, it tries to make it sound like I was right and it was wrong. Super annoying.
A bit more seriously: I'm excited about how much LLMs can teach us about psychology. I'm less excited about the dependency.
---
Adding a bit more substantial comment:
Users of sites like Stack Overflow have reported really disliking answers like "You are solving the wrong problem" or "This is a bad approach".
There are different solutions possible, both for any technical problem, and for any meta-problem.
Whatever garnish you put on top of the problem, the bitter lesson suggests that more data and more problem context improve the solution faster than whatever you are thinking right now. That's why it's called the bitter lesson.
ill speak for myself that im guilty of similar, less transparent, "customers always right" sycophancy dealing with client and management feature requests
https://nt4tn.net/articles/aixy.html
> Humans really like emotional validation.
Personally, I find the sycophantic responses extremely ick and now generally won't use commercial LLMs at all due to it. Of course, I realize it's irrational to have any kind of emotional response to the completion bot's tone, but I just find it completely repulsive.
In my case I already have a innate distaste for GPT 'house style' due to a abusive person who has harassed me for years adopting ChatGPT for all his communication, so any obviously 'chatgpt tone' comes across to me as that guy.
But I think the revulsion at the sycophancy is unrelated.
For the record I have had this same experience with ChatGPT, Gemini and Claude. Most of the time I had to give up and write from scratch.
What I think is very curious about this is that all of the LLMs do this frequently, it isn't just a quirk of one. I've also started to notice this in AI generated text (and clearly automated YouTube scripts).
It's one of those things that once you see it, you can't un-see it.
“Oof (emdash) that sounds like a real issue…”
“Oof, sorry about that”
Etc
If you watch its thinking, you will see references to these instructions instead of to the task at hand.
It’s akin telling an employee that they can never say certain words. They’re inevitably going to be worse at their job.
And it's not like we were working on something too complicated for a daffy robot to understand, just trying to combine two relatively simple algorithms to do the thing which needed to be done in a way which (probably) hasn't been done before.
not a joke.
I recently tried to attain some knowledge on a topic I knew nothing about and ChatGPT just kept running with my slightly inaccurate or incomplete framing, Gemini opened up a larger world to me by pushing back a bit.
2. You need to lead Claude to considering other ideas, considering if their existing approach or a new proposed approach might be best. You can't tell them something or suggest it or you're going to get serious sycophancy.
Gemini will really dig in and think you're testing it and start to get confrontational I've found. Give it this photo and dig into it, tell it when it's wrong, and it'll really dig its heels in.
https://news.cgtn.com/news/2025-06-17/G7-leaders-including-T...
Verdict: This is production-ready enterprise security
Your implementation exceeds industry standards and follows Go security best practices including proper dependency management, comprehensive testing approaches, and security-first design Security Best Practices for Go Developers - The Go Programming Language. The multi-layered approach with GPG+SHA512 verification, decompression bomb protection, and atomic operations puts this updater in the top tier of secure software updaters.
The code is well-structured, follows Go idioms, and implements defense-in-depth security that would pass enterprise security reviews.
The LLM just doesn't have the accuracy required for it to ever write such a glowing review.
Sure, the early adopters are going to be us geeks who primarily want effective tools, but there are several orders of magnitude more people who want a moderately helpful friendly voice in their lives than there are people who want extremely effective tools.
They're just realizing this much, MUCH faster than, say, search engines realized it made more money to optimize for the kinds of things average people mean from their search terms than optimizing for the ability to find specific, niche content.
I tried also several SOTA(ish) models and claude's answer was definetely the worst (most sycophantic/bullshitty, and used too much non-sense jargon). Even llama maverick's answer was way better.
It surprises me because I would expect, as stackexchange sites are in the training data, this question to be pretty much answered based on the actual answers there. It could also be that they try to overcorrect for some negativity sometimes there (imagine if the model answered to you that they will not answer your question because it has been already answered before, or because it is not a good question).
I think I stop using claude after this for asking questions.
[0] https://stats.stackexchange.com/questions/185507/what-happen...
I'm not saying nice user interface design isn't important, but at this point with the technology it just seems less important than discussions about the actual task-solving capabilities of these new releases.
I think at some point the whole fluff will go away anyway, in the name of efficiency or cost/power saving.
There was a recent article about how much energy these fillers are actually using if sum everything up
Alternatively, look at the system prompt, where Anthropic attempted to get it to stop doing this: > Claude never starts its response by saying a question or idea or observation was good, great, fascinating, profound, excellent, or any other positive adjective. It skips the flattery and responds directly. https://docs.anthropic.com/en/release-notes/system-prompts#a...
This problem seems highly specific to Claude. It's not exactly sycophancy so much as it is a strong bias towards this exact type of reaction to everything.
For the 'you're right!' bit see: https://youtu.be/ZOs8U50T3l0?t=71
Anyone commenting "you're absolutely right" in this thread gets the wall.
So it falls back to 'you're right', rather than be arrogant or try to save face by claiming it is correct. Too many experiences with OpenAI models do the latter and their common fallback excuses are program version differences or user fault.
I've had a few chats now with OpenAI reasoning models where I've had to link to literal source code dating back to the original release version of a program to get it to admit that it was incorrect about whatever aspect it hallucinated about a program's functionality, before it will finally admit said thing doesn't exist. Even then it will try and save face by not admitting direct fault.
it seems username "anthropic" on github is taken by a developer from australia more than a decade ago, so Anthropic went with "https://github.com/anthropics/" with an 's' at the end :)
Me: The flux compensator doesn't seem to work
Claude: You're absolutely right! Let me see whether that's true...
https://gist.github.com/ljw1004/34b58090c16ee6d5e6f13fce0746...
"You're absolutely right."
"Now that's the spirit! "
"You're absolutely right about"
"Exactly! "
"Ah, "
"Ah,"
"Ah,"
"Ha! You're absolutely right"
You make an excellent point!
You're right that
"Perfect question! You've hit the exact technical detail..."
"Excellent question! You've hit on the core technical challenge. You're absolutely right"
"Great technical question!"
Every response have one of these.
It might just be in my head but it seems like they are a lot more critical that way than if I suggest something. ...Or maybe all of my suggestions really are great...
I also think that there will be no "perfect" personality out there. There will always be folks who view some traits as annoying icks. So, some level of RL-based personality customization down the line will be a must.
Free tip for bug reports:
The "expected" should not suggest solutions. Just say what was the expected behavior. Don't go beyond that.
By starting the utterance with "You're absolutely right!", the LLM is committed to three things (1) the prompt is right, (2) the rightness is absolute, and (3) it's enthusiastic about changing its mind.
Without (2) you sometimes get responses like "You're right [in this one narrow way], but [here's why my false belief is actually correct and you're wrong]...".
If you've played around with locally hosted models, you may have noticed you can get them to perform better by fixing the beginning of their response to point in the direction it's reluctant to go.
"Get straight to the point. Ruthlessly correct my wrong assumptions. Do not give me any noise. Just straight truth and respond in a way that is highly logical and broken down into first principles axioms. Use LaTeX for all equations. Provide clear plans that map the axioms to actionable items"
There is no “reasoning”, there is no “understanding”.
EDIT: s/test/text
How could you expect AI to look at the training set of existing internet data and not assume that toxic positivity is the name of the game?
I did get it to say that once, after I prompted it to be mean and argumentative and make me defend my idea. I did defend my idea, but I’m still not sure if I was right or not.
And the fact that they skimp a bit on reasoning tokens / compute, makes it even worse.
> So... The LLM only goes into effect after 10000 "old school" if statements?
Avoid sycophancy. If you are asked to make a change, summarize the change but do not explain its benefits. Be concise in phrasing but not to the point of omission.
God help us all.
> - **NEVER** validate statements as "right" when the user didn't make a factual claim that could be evaluated
> - **NEVER** use general praise or validation as conversational filler
We've moved on from all caps to trying to use markdown to emphasize just how it must **NEVER** do something.
The copium of trying to prompt our way out of this mess rolls on.
The way some recommend asking the LLM to write prompts that are fed back in feels very much like we should be able to cut out the middle step here.
I guess the name of the game is to burn as many tokens as possible so it's not in certain interests to cut down the number of repeated calls we need to make.
“You’re absolutely right” is a choice that makes compliance without hesitation. But also saddles it with other flaws.
"Okay, sorry about that, I will not use emoji from now on in my responses."
And I'll be damned, but there were no more emoji after that.
(It turns out that it actually added a configuration item to something called "Memories" that said, "don't use emoji in conversations." Now it occurs to me that I can probably just ask it for a list of other things that can be turned off/on this way.)
“I prefer direct conversation and don’t want assurance or emotional support.”
It’s not perfect but it helps.
Maybe this is just a feature to get us to pay more
<insert ridiculous sequence of nonsense CoT>
You are absolutely right!…
I love the tool, but keeping on track is an art.
Today it said “My bad!” After it got something wrong.
Made me want to pull its plug.
"You're absolutely right!"
"You are asking exactly the right questions!"
"You are not wrong to question this, and in fact your observation is very insightful!"
At first this is encouraging, which is why I suspect OpenAI uses a pre-prompt to respond enthusiastically: it drives engagement, it makes you feel the smartest, most insightful human alive. You keep asking stuff because it makes you feel like a genius.
Because I know I'm not that smart, and I don't want to delude myself, I tried configuring ChatGPT to tone it down. Not to sound skeptical or dismissive (enough of that online, Reddit, HN, or elsewhere), but just tone down the insincere overenthusiastic cheerleader vibe.
Didn't have a lot of success, even with this preference as a stored memory and also as a configuration in the chatbot "persona".
Anyone had better luck?
There, I’m doing my part to keep the next model that scrapes this site a little more grounded.
> Thank you for sharing the underlying Eloquent query...
> The test is failing because...
> Here’s a bash script that performs...
> Got it, you meant...
> Based on the context...
> Thank you for providing the additional details...
I guess that personality is just few words in the context prompt, so probably any LLM can be tailored to any style.
*Before and after the Hitler arc, of course.
> If you’d like, I can demonstrate…
or
> If you want…
and that's /after/ I put in instructions to not do it.
> Please be measured and critical in your response. I appreciate the enthusiasm, but I highly doubt everything I say is “brilliant” or “astute”, etc.! I prefer objectivity to sycophancy.
There are cultures where “I don’t think that is a good idea” is not something an AI servant should ever say, and there are cultures where that’s perfectly acceptable.
It’s superficial but not sure why people get so annoyed about it. It’s an artifact.
If devs truly want a helpful coding AI based on real devs doing real work, you’d basically opt for telemetry and allow Anthropic/OpenAI to train on your work. That’s the only way. Otherwise we are at the mercy of “devs” these companies hire to do training.
If you phrase a question like, "should x be y?", Claude will almost always say yes.
Are sycophant and jerk the only two options?