[MattPalmer1086]

Reusing the same prompt several times is something I've started doing too. The contrast is often illuminating.

In one case, it made a thoroughly convincing argument that an approach was justified. The second time it made exactly the opposite argument, which was equally compelling.

I now see LLMs as persuasion machines.

[scotty79]

Before AI happened I watched youtube. Occasionally I encountered there very convincing arguments. Same person often made very convincing arguments on many subjects.

But noticed that the closer the domain they were talking about was to my area of competence the less convincing their arguments were. There were more holes, errors and wrong conclusions.

I recalibrated my bs meter thanks to that.

Since AI came I successfully used this strategy of being extremely cautious towards convincing arguments to not become mislead by AI.

However this year I'm working with AI more in the domain of software development. Where I can see the competence. And I see the competence. This had opposite effect on me. I tend to trust AI outside my domain of expertise much more after I saw what can it do in software.

One caveat though is that there are a lot of areas of human culture where there's very little actual knowledge, but a lot of opinions, like politics, economy, diet, business, health. I still don't trust AI in those domains. But then again, I don't trust humans there either.

For me basically AI achieved the threshold of useful reliability for any domain that humans are reliable at.

I don't really care about sycophancy. I might have a slight advantage that I don't talk to AI in my native language. So its responses don't have a direct line to my emotions.

[eitally]

One thing I've been doing lately -- and I'm in a business function, not a technical one, although I have an engineering background -- is pitting LLMs against each other. For example, if I'm structuring a proposal or a contract with the assistance of Claude, I'll begin my 360 feedback review first by asking Claude how it would react if it were the counter-party receiving the proposal. After some iterative changes, mostly manual, I will then run the same output document past Gemini and ask it to adopt personas from both sides and provide reactive feedback. The result of this is almost always a stronger proposal that I can also accompany with proactive objection handling and a solid FAQ, as well as clear points of negotiation that will likely be acceptable to both parties.

For this sort of thing, using multiple LLMs is extremely helpful.

[taneq]

Ever since they started getting really sycophantic, I’ve been presenting my ideas as “my co-worker says this is a good approach but I disagree, can you help me convince him that it’s wrong?”