I think i’ve only once flat out told one it was wrong about a specific assertion I quoted and it immediately was able to find its way to what I knew to be the correct claim.
I just wonder what would happen if i was in fact mistaken and I told it confidently it was wrong without elaborating
It depends on training, prompting and reasoning capabilities.
Sometimes they’re prompted to not be assertive. You can often tell them though to e.g. “behave like an XYZ expert in a bad mood who doesn’t accept nonsense”.
I’ve had e.g. ChatGPT contest me a lot even though I was right. It was about a bicycle brake design I had never seen before. It gave me some options of what it could be and helped me to actually find out what it was. However after I did some research and found out what the actual type was it kept doubting my result and insisted it was a different kind of brake.
I’m trying not to judge and to just remain curious here. Why would you keep using AI like that?
You can use LLMs for things that where not possible or very difficult with traditional search engines.
E.g. I wanted to know what kind of brake my daughters bike used. Traditionally I’d either have to research all possible brake types and compare then with her bike or take a photo and post it to a forum or reddit or something and hope someone knows the answer.
With ChatGPT (I only use the free version) I just took a photo and asked what kind of break it was and got a (actually good) list of 2 possible brakes. It was one of the two.
Very convenient. However, I’m aware how LLMs work and what their limitations are. Of course also the environmental issues.
BTW: It was a band brake.
Because search engines have been dogshit for the past 2 years (give or take~); AI need to be steered hard but as long as you’re asking for sources (and don’t just read what they’re saying) you can get an answer out of them.
I only find what I’m searching for on Google if I already know the exact keywords for what I’m specifically looking for (…and even then…); if I don’t know the exact terms of what I’m looking for (like @affenlehrer@feddit.org with his bike brake issue) then google is useless nowadays, which wasn’t always true. So now my process is to google first, ask an AI second, and I end up using AIs way more than I would like.
It led them to the right answer. That’s positive reinforcement.
The best sort of methodology I’ve found to coerce Claude or whatever (we are strongly encouraged to use it, because you know, tech these days) is (for a single agent) to define a process that includes proving its work and citing sources. For agentic flow, you basically just assign a contrarian role in particular domains to some of the agents - ideally all of this is also hooked into an MCP server that includes deterministic utilities to improve accuracy and solution arrival speed.
It’s basically just a shitty, brute-forced, massively over complicated Monte Carlo algorithm that’s wildly inefficient in terms of energy usage and infrastructural cost, that also happens to be turning our economy into a highly flammable house of cards.
Can you tell what my opinion of all this bullshit is, despite knowing how to do all of this crap reasonably well? 😛
I think that’s a good approach. Personally I find LLMs quite fascinating but they’re deeply flawed. They can barely be used in production environments, especially unsupervised. The workflows regarding LLMs are very esoteric with specific prompting techniques etc and while all LLMs have similar flaws each model and model version behaves differently. It’s super weird and unreliable. Like one big workaround that has so much investment that it keeps improving every month but still stays shitty at it’s base.