Relevant: https://en.wikipedia.org/wiki/Wikipedia:Don%27t_stuff_beans_...

Don't put marbles in your nose

https://www.youtube.com/watch?v=xpz67hBIJwg

I remember seeing a father loudly and strongly tell his daughter "DO NOT EAT THIS!" when holding one of those desiccant packets that come in some snacks. He turned around and she started to eat it.

Quick, don't think about cats!

> it always mentions something about how it’s going to be ‘straight to the point’ or give me a ‘no bs version’

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.

Any time you're fighting the training + system prompt with your own instructions and prompting the results are going to be poor, and both of those things are heavily geared towards being a cheery and chatty assistant.

I had instructions added too and it is doing exactly what you say. And it does it so many times in a voice chat. It's really really annoying.

No fluff

Default is

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.

Just got stung with this on GPT5 - It’s new prompt personalisation had “Robotic” and “no sugar coating” presets.

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’re giving them way too much agency. The don’t love anything and cant be malicious.

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”.

This is true, but I still avoid using examples. Any example biases the output to an unacceptable degree even in best LLMS like Gemini Pro 2.5 or Claude Opus. If I write "try to do X, for example you can do A, B, or C" LLM will do A, B, or C great majority of the time (let's say 75% of the time). This severely reduces the creativity of the LLM. For programming, this is a big problem because if you write "use Python's native types like dict, list, or tuple etc" there will be an unreasonable bias towards these three types as opposed to e.g. set, which will make some code objectively worse.

I almost never use examples in my professional LLM prompting work.

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.

Seems like a lot of work, though.

It reminds me of that old joke:

- "Say milk ten times fast."

- Wait for them to do that.

- "What do cows drink?"

I hope he did not say it _to_ the unconscious. I count three negations there...

Nietzsche said it way better.

I liked the completion models because they have no chatter that needs to follow human conversational protocol, which inherently introduces "personality".

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.

Yeah different system prompts make a huge difference on the same base model”. There’s so much diversity in the training set, and it’s such a large set, that it essentially equals out and the system prompt has huge leverage. Fine tuning also applies here.

That your firm purchased training that was clearly just some chancers doing whatever seems like an even worse approach than just giving out access to a service and telling everyone to give it a shot.

Do they also post vacancies asking for 5 years experience in a 2 year old technology?

I think you are really onto something here - I bet this would also reliably work when talking to humans. Maybe this is not even specifically the fault of the AI but just a language thing in general.

An alternative test could be prompting the AI with "Avoid not" and then give it some kind of instruction. Theoretically this would be telling it to "do" the instruction but maybe sometimes it would end up "avoiding" it?

Now that I think about it the training data itself might very well be contaminated with this contradiction.......

I can think of a lot of forum posts where the OP stipulates "I do not want X" and then the very first reply recommends "X" !

This reminds me of a phenomena in motorcyling called "target fixation".

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.

Given how LLMs work it makes sense that mentioning a topic even to negate it still adds that locus of probabilities to its attention span. Even humans are prone to being affected by it as it's a well known rhetorical device [1].

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.

1: https://en.wikipedia.org/wiki/Apophasis

> You're absolutely right!

Claude?

> You're absolutely right!

Is this irony, actual LLM output or another example of humans adopting LLM communication patterns?

I think its an english language thing (or language in general).

Someone above commented about using the word "Avoid" instead of "do not". "Not" obviously means you should do the opposite but the first word is still a verb telling you to take action.

I must be dyslexic? I always read, "Silica Gel, Eat, Do Not Throw Away" or something like that.

I used to have fast enough reflexes that when someone said “do not think of” I could think of something bizarre that they were unlikely to guess before their words had time to register.

So now I’m, say, thinking of a white cat in a top hat. And I can expand the story from there until they stop talking or ask me what I’m thinking of.

I think though that you have to have people asking you that question fairly frequently to be primed enough to be contrarian, and nobody uses that example on grown ass adults.

Addiction psychology uses this phenomenon as a non party trick. You can’t deny/negate something and have it stay suppressed. You have to replace it with something else. Like exercise or knitting or community.