Top-K and Top-P sampling are methods to generate text by limiting options to the most likely words. Top-K picks from the top K words based on probability. Top-P (nucleus sampling) selects from the smallest set of words whose total probability exceeds P, dynamically adjusting the size.
During text generation, the model ranks possible next words by likelihood. Top-K samples only from the fixed number K highest. Top-P sums probabilities from the top until it reaches P (e.g., 0.9) and samples within this flexible set. This reduces unlikely or repetitive words, balancing creativity and coherence.
For product managers, choosing sampling impacts user experience by controlling text diversity and relevance. It affects compute costs and latency since smaller candidate sets mean faster generation. Proper tuning helps scale AI features reliably while managing risk of nonsensical output, enhancing product value.