That’s where you’re wrong. Raw LLM very often get stuck in loops, usually extremely small loops. There’s a big chunk of infrastructure that exists on the output

That’s where you’re wrong. Raw LLM very often get stuck in loops, usually extremely small loops. There’s a big chunk of infrastructure that exists on the output end of any production LLM that exists explicitly for the purpose of preventing loops. That post-processing infrastructure can use all kinds of mechanisms to prevent loops and induce more useful output. With the most basic simply systems simply refusing to select any output token that already in the input, to more complex stochastic process that explore the tree of possible outputs, to find branches whose overall result is improved by choosing less optimal immediate steps. The vast majority of what make Chat GPT different to simpler GPT-3 models is this complex post-processing phase that allows designers to push and pull on the behaviour of the pre-baked static model underlying the chat interface.