Sampling-based Inference — Why Probabilistic Token Selection Matters in LLM Decoding

Sampling-based Inference is a decoding strategy that determines how an LLM chooses the next token during inference. When a language model receives a prompt, it does not immediately settle on a single next token. Instead, it scores many possible candidates and converts those scores into a probability distribution. Rather than always taking the most likely token, sampling draws from that distribution. As a result, the same prompt can follow different generation paths and produce different outputs.

09/13/2026