The complete post is available where it was originally published on this site
Tuning LLM outputs is largely a decoding problem: you shape the model’s next-token distribution with a handful of sampling controls:
max tokens (caps response length under the model’s context limit)
temperature (logit scaling for more/less randomness)
top-p/nucleus and top-k (truncate the candidate set by probability mass or rank)
frequency and presence penalties (discourage repetition or encourage novelty)
stop sequences (hard termination on delimiters). These seven parameters interact: temperature widens the tail that top-p/top-k then crop; penalties mitigate degeneration during long generations; stop plus max tokens provides deterministic bounds.
The complete article defines each parameter precisely and summarize vendor-documented ranges and behaviors grounded in the decoding literature.
