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.
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.