How LLMs Actually Know When to Stop

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 #474777 — open it directly at myweirdprompts.com/474777