Comment by mystified5016 | Hacker News Reader

Comment by mystified5016 | Hacker News Reader

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The biggest thing I've found is that if you give any hint at all as to what you think the problem is, the LLM will immediately and enthusiastically agree, no matter how wildly incorrect your suggestion is.

If I give it all my information and add "I think the problem might be X, but I'm not sure", the LLM always agrees that the problem is X and will reinterpret everything else I've said to 'prove' me right.

Then the conversation is forever poisoned and I have to restart an entirely new chat from scratch.

98% of the utility I've found in LLMs is getting it to generate something nearly correct, but which contains just enough information for me to go and Google the actual answer. Not a single one of the LLMs I've tried have been any practical use editing or debugging code. All I've ever managed is to get it to point me towards a real solution, none of them have been able to actually independently solve any kind of problem without spending the same amount of time and effort to do it myself.

> The biggest thing I've found is that if you give any hint at all as to what you think the problem is, the LLM will immediately and enthusiastically agree, no matter how wildly incorrect your suggestion is.

I'm seeing this sentiment a lot in these comments, and frankly it shows that very few here have actually gone and tried the variety of models available. Which is totally fine, I'm sure they have better stuff to do, you don't have to keep up with this week's hottest release.

To be concrete - the symptom you're talking about is very typical of Claude (or earlier GPT models). o3-mini is much less likely to do this.

Secondly, prompting absolutely goes a huge way to avoiding that issue. Like you're saying - if you're not sure, don't give hints, keep it open-minded. Or validate the hint before starting, in a separate conversation.

I literally got this problem earlier today on ChatGPT, which claims to be based on o4-mini. So no, does not sound like it's just a problem with Claude or older GPTs.

And on "prompting", I think this is a point of friction between LLM boosters and haters. To the uninitiated, most AI hype sounds like "it's amazing magic!! just ask it to do whatever you want and it works!!" When they try it and it's less than magic, hearing "you're prompting it wrong" seems more like a circular justification of a cult follower than advice.

I understand that it's not - that, genuinely, it takes some experience to learn how to "prompt good" and use LLMs effectively. I buy that. But some more specific advice would be helpful. Cause as is, it sounds more like "LLMs are magic!! didn't work for you? oh, you must be holding it wrong, cause I know they infallibly work magic".

> I understand that it's not - that, genuinely, it takes some experience to learn how to "prompt good" and use LLMs effectively

I don't buy it this at all.

At best "learning to prompt" is just hitting the slot machine over and over until you get something close to what you want, which is not a skill. This is what I see when people "have a conversation with the LLM"

At worst you are a victim of sunk cost fallacy, believing that because you spent time on a thing that you have developed a skill for this thing that really has no skill involved. As a result you are deluding yourself into thinking that the output is better.. not because it actually is, but because you spent time on it so it must be

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