Post #42653

2025-12-08 19:18 UTC

I don’t know shit about anything, but it seems to me that the AI already thought it gave you the best answer, so going back to the problem for a proper answer is probably not going to work. But I’d try it anyway, because what do you have to lose? Unless it gets pissed off at being questioned, and destroys the world. I’ve seen more than few movies about that.

Replies (2)

@Evotech@lemmy.world 2025-12-08 19:30

You are in a way correct. If you keep sending the context of the “conversation” it will reinforce its previous implementation. But once you start a new conversation "meaning you fint give any previous chat history " it’s essentially a new ai. With a new random seed and if you ask that to look for mistakes etc it will happily tell you that the last Implementation was all wrong and here’s how to fix it. It’s like a minecraft world, same seed will get you the same map every time. So with AIs it’s the same thing ish. start a new conversation or ask a different model (gpt, Google, Claude etc) and it will do things in a new way.

Open ##42686
@MangoCats@feddit.it 2025-12-09 14:35

AI already thought it gave you the best answer, so going back to the problem for a proper answer is probably not going to work. There’s an LLM concept/parameter called “temperature” that determines basically how random the answer is. As deployed, LLMs like Claude Sonnet or Opus have a temperature that won’t give the same answer every time, and when you combine this with feedback loops that point out failures (like compliers that tell the LLM when its code doesn’t compile), the LLM can (and does) the old Beckett: try, fail, try again, fail again, fail better next time - and usually reach a solution that passes all the tests it is aware of. The problem is: with a context window limit of 200,000 tokens, it’s not going to be aware of all the relevant tests in more complex cases.

Open ##45453