The Case for Preprocessing: Using LLMs Before Your Users Do
Learn how to use LLMs efficiently by preprocessing and storing AI-generated content on the backend instead of generating it on demand, saving costs, reducing latency, and improving scalability.
Most of us interact with LLMs today through chat interfaces. We type in whatever’s on our mind (e.g. random questions, half-formed thoughts) and the AI responds with something uniquely tailored, almost instantly. It’s beyond impressive. But this immediacy also shapes how we think about using LLMs: as tools that only operate in real time, responding to a…