Post #45453

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.