What developers don't realize until their queue is on fire? I thought I understood the problem. You've got a list of inputs. You send each one to an LLM API. You get text back. You store it. Done — right? That's what I believed until I tried to run it at scale. Not "scale" in the Silicon Valley sense. Just: 400 product descriptions for a client's e-commerce migration, needed by Thursday, with a two-person team and a $30 API budget. The naive version worked fine for the first 40. Then it quietly started failing in ways that took me four hours to even name. This is what bulk text generation actually involves — the parts that don't show up in any "getting started with the OpenAI API" tutorial. The four problems nobody warns you about Rate limits are not what you think they are The OpenAI rate limit docs say: "You can make X requests per minute." That sounds like a traffic light — green until you hit the limit, then red. It's not a traffic light. It's more like a leaky bucket with multiple dimensions.…