LLM: How Response Generated - Sampling

Sampling governs how a model chooses the next token from its probability distribution, which directly affects quality, consistency, and variation.

Temperature, top-k, and top-p each shape output diversity differently, and they must be tuned to the target task instead of treated as static defaults.

Constrained decoding becomes especially useful for structured tasks, where free-form generation can produce invalid or unsafe outputs.

The practical takeaway is that decoding strategy is part of product behavior, not just a model parameter.