Ever wondered why an AI model doesn't just keep generating text forever? The answer is surprisingly fragile. This episode breaks down the three layers that make LLMs stop: the probabilistic EOS token the model learns during training, the inference-engine stop sequences that can yank the plug mid-sentence, and the brute-force context window limit. We explore why base models ramble while fine-tuned models seem decisive, how sampling parameters like temperature can break the stop mechanism entirely, and why multi-modal and agentic systems need entirely different approaches to knowing when to quit.
Episode 4789 — myweirdprompts.com/n/4789 (permanent short link: myweirdprompts.com/474777)
