Why the Same Model Almost Never Gives the Same Answer Twice
Same prompt, same model, twice, two different answers. Not a bug: the model draws from a probability distribution at every word. What temperature, top-p, and top-k actually set.
Same prompt, same model, twice, two different answers. Not a bug: the model draws from a probability distribution at every word. What temperature, top-p, and top-k actually set.
One request answers instantly, then drips out word by word. Another takes a while before the first word appears, then runs through fast. The reason: two phases with different bottlenecks.
An AI model never sees an image as pixels. It cuts it into a grid of tiles that become numbers, and reads them like tokens. Why the fine print disappears first.
Same model, same question. Every answer costs more the longer the conversation runs. The reason has a name: KV cache.
A token is the smallest unit a language model splits text into. Why the model sees no letters, and what that one fact explains about cost and errors.