Most developers use LLM APIs like a black box: pick a model, send requests, and live with whatever performance they get. This talk argues that this is the wrong way to think about performance. Modern inference stacks implement optimizations such as prompt caching, speculative decoding, disaggregated inference, and more. But for those optimization gains to be maximized, the application needs to use the API in the right way. I will explain the key optimizations that matter, how they work at a high level, and what API users can do to fully benefit from them in practice. The focus is not on theory or provider internals. It is on helping practitioners get better real-world performance from the same LLM APIs.