Sampling Changes the Answer Space
Explain how model scores become token probabilities and why sampling settings change output variability.
The same product prompt produces different brand adjectives, and the team needs to understand why. PDCA for sampling: plan the desired variance, test settings, check output quality, act by standardizing per task. The common trap is using one sampling setting for every task because it is the default in the tool. Scores For the next token, the model scores options such as " confident", " calm", " premium", and " playful". These raw scores are often called logits. Probabilities The scores are transformed into a probability distribution over possible next tokens. High-probability tokens fit the context more strongly according to…
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